Theories of Lean and Leveling/Heijunka| Christoph Roser

ChristophRoser-200x300Christoph Roser has more impressive credentials than most Lean consultants, from a PhD in Engineering to a research job at Toyota labs, stints in operations at Bosch, and a professorship at Karslruhe University of Applied Sciences. So, if anyone is qualified to write a theory of Lean, he is, and he is trying his hand at it in production planning and scheduling.

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Change your production leveling strategy to achieve flow | Ian Glenday | Planet Lean

“…What I came to call Repetitive and Flexible Supply (RFS) is based on the idea of manufacturing the largest products in the same sequence at the same time every week. To many people, this sounds ridiculous and stupid at first.

My analysis consistently showed that, typically, 6% of a company’s products represent 50% of the volume it produces.

I started to see this happen in every factory, hospital, or office I went to. And that’s when it hit me – why not simply focus on stabilizing the plan for that 6% of the products?…”


Michel Baudin‘s comments:

Ian Glenday’s idea of RFS is fine, but not quite as original as presented in the article. Making it easy to do what you do the most often is the motivation behind the Product-Quantity (P-Q) analysis I learned in Japan in the 1980s.

To use the terminology introduced  in the UK by Lucas Industries about that time, it breaks the product mix into Runners, Repeaters, and Strangers. You make each Runner is an dedicated production line, because it has a volume that justifies it.

Then you group Repeaters in families and make them in flexible lines, and you keep a residual job-shop to make the Strangers — the long tail of your demand — products in large numbers but with low and sporadic demand.

This method is described, as prior work, in Lean Assembly as a foundation for assembly line design, and in Lean Logistics for warehouse/supermarket design and for production scheduling, in particular heijunka.

See on Scoop.itlean manufacturing

Supermarket sizing

Bosch’s Taojie Hua (涛杰 华) asked the following question:

How do you define a maximum limit for a supermarket?
Especially when the customer withdraws less than planned, and the lot can not be formed as a production signal, how can I react to that “deviation” by setting a proper max limit?

The response covers the following topics:

Supermarkets in Lean

First we have to clarify what we mean by a supermarket in a Lean manufacturing context. As the term has become popular, some plants have started using it for their warehouses, which is clearly excessive. Often, it is used for any kind of buffer on the shop floor, provided it is used to implement pull. I prefer to reserve the term for buffers from which users withdraw items in smaller quantities than are brought in. If pallets come in and go out, I don’t call it a supermarket, but, if 27-bin pallets come in and withdrawals take place 1 bin at a time, I do.

On a shop floor, supermarkets are found on the edges of manufacturing islands containing a group of cells or a production line and contain either incoming or outgoing materials.  A supermarket for incoming materials has more in common with the refrigerator in your kitchen than with the supermarket you buy groceries in. You need one when your plant Materials or Logistics organization is unable to deliver materials in a form that is suitable for direct use at a production work station.

Water spider at Solectron in  Mexico (2005)

Water spider at Solectron in Mexico (2005)

The supermarket is owned by Production, and more specifically by the first-line manager in charge of the cells or lines it serves. It is replenished  by  Materials or Logistics through periodic milk runs, but parts are withdrawn by experienced members of the production team — cell leaders or water spiders — and move from the supermarket to production on hand carts, gravity flow racks, or by hand. The parts arrive in the supermarkets in bins that are too large for the line side, and leave in kit trays, small bins, or single units.

You need a supermarket for outgoing materials when your production runs are multiples of the quantities needed downstream. This happens, for example, if you only know how to paint parts in batches of 50 with the same color, while assembly alternates colors one unit at a time. In outgoing supermarkets, materials are replenished by Production and withdrawn by Materials/Logistics.

Supermarket capacity

For incoming supermarkets, replenishment by milk runs is essential because it makes lead times predictable. I am assuming here that the upstream supply chain does not cause shortages. Making it work is no small feat, but this question is specifically on supermarkets. On the withdrawal side, you want to have the smoothest possible consumption rate for all items, so that you don’t have large ups and downs to contend with, which you achieve with  heijunka (平準化)  sequencing of production. Little’s Law then tell you that you have, for means:

\overline{Quantity\, on\, hand}\left ( Item \right )= \overline{Consumption\, rate}\left ( Item \right )\times \overline{Replenishment\, lead\, time}\left ( Item \right )

If you take the minimum quantity that Materials can deliver to the supermarket, on the average the Quantity on hand will be half of it. You know the Consumption Rate.  The Replenishment lead time is a multiple of the milk run pitch, plus the time needed for Materials to act on the pull signal, which depends on when the need is identified and how the signal is passed to Materials.

Assume you consume 1 unit every 25 seconds, the milk run pitch is 30 minutes, and Materials delivers in bins of 100 units. You consume 72 parts/pitch = 0.72 bins/pitch. If the milk runs are used to convey pull signals, as happens with the two-bin system or with hardcopy kanbans, replenishment may take up to 2 pitches. In this example, the 2-bin system would cause shortages, but a Kanban loop with two cards wouldn’t, because you pull the card when you withdraw the first unit from the bin and it is still 99% full. If, instead of using cards, you issue an electronic signal when you withdraw the first unit, Materials can act on it in the next milk run, meaning at most 1 pitch later. You still need room for two bins, because the current bin will still hold at least 28 parts when the replacement bin arrives.

In this example, the mismatch between the size of the delivered bins and the consumption rate forces you to hold enough excess material that you don’t need to worry about safety stocks. If it were instead perfectly matched, you could receive a bin of 72 parts like clockwork every 30 minutes, except that fluctuations in consumption occasionally would cause shortages, and you would need some safety stock to protect yourself against it.  Coming up with a sensible plan for any one item in your supermarket is not a major task, but you need such a plan for every item.

The speed with which signals circulate adjusts itself with fluctuations in consumption. The real question is whether your “customer withdrawing less than planned” should be treated as a fluctuation or a permanent drop. In the first case, there is no action required; in the second, you need to recalculate.  In any case, you need to periodically validate the parameters of your pull system to make sure they still reflect reality. In auto parts, it should be done at least quarterly.

Further reading

For details on pull systems, see Lean Logistics, Part IV, pp. 197-330. See also the two posts on Safety Stocks: Beware of Formulas and Safety Stocks: More about the formula.

Mitigating “Mura,” or unevenness

The Japanese word Mura (ムラor斑) is the third member of the Muri, Muda, Mura axis of manufacturing evils. It means unevenness. In terms of volume of activity, if Muri refers to overburdening resources, Mura then really is the conjunction of overburdening some resources while others wait, or of alternating over time between overburdening and underutilizing the same resources.

Unevenness, however, is not only about volumes, but about quality as well. Unevenness in products is even synonymous with bad quality. From production managers facing “unpredictable” environments to academics promoting genetic algorithms or other cures, everyone bemoans how variable, or uneven, manufacturing is. The litany of causes is endless. Following are a few points that I think may clarify the issues:

  1. Mura in space, Mura in time, and Mura in space and time
  2. Degrees of severity: Deterministic, random, and uncertain environments
  3. What is special about Manufacturing?
  4. Internal versus external causes of unevenness
  5. Most useful skills in dealing with Mura

Mura in space, Mura in time, and Mura in space and time

Mura in space is imbalance in the work loads or utilization among resources at the same time; Mura in time, variability in the work load of a resource over time. The two can be present in the same factory. You may notice a kitting team working feverishly while the next one is waiting but, two hours later, you find the roles reversed.
Mura is often symbolized by two trucks arriving in sequence with different loads. I tend to think of working with Mura as moving around a city built on hills. A city built on a plain is even and easy to cross, and is often planned with a grid of numbered streets, like Manhattan in New York, or Kyoto. A city built on hills is uneven and offers many obstacles. San Francisco is built on hills, but its planners have chosen to ignore the terrain and slap on it a grid of straight streets. It makes for great views and dramatic car chases, but its steep slopes challenge your engine, your suspension, and your parking skills. Most hilly cities, like Nagasaki, Japan, for example, instead have streets that follow contour lines and therefore meander. The path by car from point A to point B may be longer than a straight line, but it is a smooth ride.

Navigating the peaks and valleys of product demand is like driving in a hilly city. If you just go straight, you keep alternating between pressing the accelerator and the brake, but by hugging contour lines, you can reach to your destination while going at a steady pace. This is what fighting Mura is about.

Degrees of severity: Deterministic, random, and uncertain environments

Some businesses are deterministic. They are “boring” and predictable. They have no variability. A manager once explained to me the electricity meter business in the market his plant was serving, as follows: “There are 20 million households with electricity meters in the country. Each meter lasts 20 years. Every year, I have to make 1 million.” The same products are made for many years, in stable quantities, and with mature processes that have no problem meeting tolerances. Of course, it only lasts until the advent of a disruptive technology, like smart meters.

This kind of environment is not common but it does exist. If your are in one, you should focus your improvement efforts on the opportunities it offers, and avoid tools that are overkill for it. For example, large, diversified companies that make a corporate decision to deploy the same planning and scheduling system in all their plants burden their simplest and most stable business units with unnecessary complexity.

Other business have variations that can be best be described as fluctuations around a smooth trend. If you make consumer goods, the demand every day is the result of decisions from a large number independent agents and will vary in both aggregate volume and mix, but within ranges that can be predicted. In terms of quality characteristics, if you fire ceramics, they shrink, by factors that still vary, even though we have been using this process for thousands of years. This level of variability is very common. The best term to describe it is randomness, and there is a rich body of knowledge on ways to work with it, including the Kanban system to regulate fluctuating flows and techniques to adjust processes in order to obtain consistent results from materials that are not. In ceramics, for example, you make your parts from a slurry that is a moving average of batches of powders received from the supplier, in order to even out their characteristics.

Contrast this with a toy manufacturer who cannot tell ahead of time which one or two products will be hits at Christmas, when most of the year’s sales occur. In process technology, there are similar differences in variability between mature, stable industries and high technology suffering from events like “yield crashes” during which a manufacturing organization “loses the recipe” for a product. Various terms are used to describe such situations, which, following Matheron, I call uncertainty. In such circumstances, the best you can expect from the techniques used to deal with randomness is to let you know that they no longer work. For example, dealerships can shield your plants from fluctuations in consumer purchases, where direct selling would let you find out sooner when demand drops for good or when consumer tastes change.

Calling our environment deterministic, random, or uncertain is always a judgment call. The deterministic electricity meter business turns uncertain with the advent of smart meters. If you view your environment as random, you expect fluctuations with a predictable range, and the signal of a shift into uncertainty manifests itself in changes beyond this range. You can use a variety of tests to detect that such as shift has occurred. Furthermore, with the possible exception of quantum physics, randomness is always in the eye of the beholder, and not intrinsic to a phenomenon.

What is special about Manufacturing?

Manufacturing is not the only kind of business to have high overhead; others include aviation or hospitals. In all such cases, companies must invest upfront in resources that pay off over time, and this is easiest to achieve with activities that place a balanced load on all resources and don’t vary over time — that is, without Mura.

Internal versus external causes of unevenness

Some unevenness comes from outside the organization, in many forms:

  • Fickle customers.
  • Seasonal variations in demand as in the toy industry.
  • Seasonal variations in supply, such as crop seasons in the food industry.
  • Changes in the macro-economy, such as a financial crisis.
  • Natural disasters, like earthquakes, tsunamis, and floods.
  • Raw materials with uneven characteristics, like ores or electronic waste for recycling.
  • Epidemics, as when 10% of your work force has the flu.
  • Unreliable suppliers.

You do not have the power to eliminate this kind of unevenness, but you can use countermeasures to mitigate its effects. On the other hand, you can and should eliminate unevenness that is self-inflicted. If you have not paid attention to balancing the work load of the various stations on your production lines, you are likely to have both overburdened and underutilized operators. Because of the different roles machines play, the workload can rarely be balanced across machines in a line, but the workloads of operators can be.

If you order materials from suppliers, for example by relying on an ERP system to issue orders by an algorithm for timing and quantities that you don’t undertand, you may well cause alternations of feast and famine in your suppliers’ order books for materials that you, in fact, consume at a steady pace. This creates unevenness not only in your suppliers’ operations, but also in your internal logistics. In The Lean Turnaround, Art Byrne explains that, at Wiremold, he eliminated volume discounts and incentives for Sales to book the largest possible orders. Instead, he preferred a steady flow of small orders, that smoothed the aggregate demand.

Not all resources need to be treated the same way. You want resources that can be described as producers to be generating useful output all the time. Other resources, which we may call responders, must be available when needed, and this is a radically different objective. Unevenness is an enemy for producers, but, unless responders’ work loads provide enough slack, they are unable to respond. Firefighters fighting fires 100% of the time would be unavailable when a new fire breaks out, and the same logic applies to maintenance technicians and operators who work as floaters on a production line. And it applies to machines as well as people. In a machine shop, for example, machines that carry out the primary processes, like hobbing a gear or milling pockets in a slab, are producers, while devices used for secondary processes, like deburring or cleaning, are responders. This is often, but not always, related to the cost of the machines, with expensive machines as producers and cheap ones as responders. However, some of the most expensive equipment, like machining centers, may be bought for its flexibility more than for its capacity, in which case its primary role is to respond to orders for short runs or prototypes.

Most useful skills in dealing with Mura

Permanently uneven workloads among operators can be addressed by balancing, using Yamazumi charts for manual operations and work-combination charts for operations involving people and machines. If the unevenness pattern shifts or oscillates over time, then the workload itself needs to be smoothed, with is done by the various techniques known as heijunka.

Many organizations are not aware of Mura as a problem, and, when aware, are oblivious to patterns in the unevenness that can be used to mitigate or eliminate it. Management, for example, may be struggling to cope with occasional large orders and fail to notice that they arrive like clockwork every other Wednesday from the same customer. A modicum of data mining skills is needed to recognize such patterns in the records of plant activity.