How to identify the constraint of a system? Part 3

Inventories and Work In Progress (WIP) can be helpful clues to visually identify the bottleneck or constraint in a process, but they can also be insufficient or even misleading as I explained in part 2 of this series.

It is often also necessary to study material and parts routes to really understand where they get stuck and delayed. Chances are that the missing or delayed items are waiting in a queue in front of the constraint. Or have been stolen by another process…

In the search for the system’s constraint, experienced practitioners can somewhat “cut corners” by first identifying the organization’s typology among the 3 generic ones: V, A or T. Each category has a specific structure and a particular set of problems. Being aware of the specific problems and possible remedies for each of the V, A and T categories may speed up the identification of the constraint and improvement of Throughput.

V, A & T in a nutshell

Umble and Srikanth, in their “Synchronous Manufacturing: Principles for World Class Excellence”, published 1990 by Spectrum Pub Co (and still sold today), propose 3 categories of plants based on their “dominant resource/product interactions”. Those 3 categories are called V, A and T.

V, A & T plants

V, A & T plants

Each letter stands for a specific category of organization (factories, in Umble’s and Skrikanth’s book) where the raw materials are supplied mainly at the bottom of the letter and the final products delivered at the top of the letter.

V-plants

V type plants use few or unique raw material processed to make a large variety of products. V-plants have divergence points where a single product/material is transformed in several distinct products. V-plants are usually highly specialized and use capital-intensive equipment.

V-plant

V-plant

You may imagine a furniture factory transforming logs of wood into various types of furniture, food industry transforming milk in various dairy products or a steel mill supplying a large variety of steel products, etc.

The common problems in V-plants are misallocation of material and/or overproduction.

As the products, once gone through a transformation cannot be un-made (impossible to un-coock a product to regain the ingredients), thus if material is misallocated, the time to get the expected product is extended until a new batch is produced.

The misallocated products wait somewhere in the process to meet a future order requiring them or are processed to finished goods and sit in final goods inventory.

The transformation process usually uses huge equipment, not very flexible and running more efficiently with big batches. Going for local optimization (Economic Order Quantity (EOQ) for example) regardless of real orders leads to long lead times and overproduction.

V-plants often have a lot of inventories and poor customer service, especially with regards to On-Time Delivery. A commonly heard complaint is “so many shortages despite so many inventories”.

Misallocations and overproduction before the bottleneck will burden the bottleneck even more. Sales wanting to serve their upset customers often force unplanned production changes, which leads to chaos in planning and amplification of delays (and of the mess).

Identification of the bottleneck should be possible visually: Work In Progress should pile up before the bottleneck while process steps after the bottleneck are idle waiting for material to process.

Note: while the bottleneck is probably a physical resource in a transformation process, the constraint might be a policy, like imposing minimum batch sizes for instance.

A-plants

A-plants use a large variety of materials / parts / equipment (purchased and) being processed in distinct streams until sub-assembly or final assembly, that make few or a unique product: shipbuilding or motor manufacturing, for example.

A-plant

A-plant

Subassembly or final assembly is often waiting for parts or subassemblies because insuring synchronization of all necessary parts for assembly is difficult. Expediters are sent hunting down the missing parts.

Expediting is likely to disrupt the schedule on a machine, a production line, etc. If the wanted part is pushed through the process, it is at the expense of other parts that will be late. The same will repeat as the chaos gets worse.

In order to keep the subassembly and assembly busy, planning is changed according to the available kits. Therefore some orders are completed ahead of time while others are delayed.

The search for the bottleneck(s) starts from subassembly or final assembly based on an analysis of the delays and earlies. Parts and subassemblies that are used in late as well as in early assemblies are not going through the bottleneck. Only parts constantly late will lead to the bottleneck. For those, follows the upstream trail until finding the faulty resources where the queue accumulates.

T-plants

T type factories have a relatively common base, usually fabrication or assembly of subassemblies and a late customization / variant assembly ending in a large display of finished goods. Subassemblies are made to stock, based on forecasts while final assembly is made to order and in a lesser extend made to stock. In this latter case it’s to keep the system busy even there are no sufficient orders. Assembly is made to stock for the top-selling models.

T-plant

T-plant

Computers assembled on-demand for instance use a limited number of components, but their combinations allow a large choice of final goods.

In order to swiftly respond to demand, final assembly generally has excess capacity, therefore the bottleneck is more likely to be found in the lower part – subassemblies – of the T.

The top and bottom of the T-plants are connected via inventories acting as synchronization buffers. The identification of the bottleneck(s) starts at the final assembly with the list of shortages and delayed products. The components or subassemblies with chronic shortages or long delays point to a specific process. The faulty process must then be visited until finding the bottleneck.

Yet bear in mind that assembly cells, lines or shops may “steal” necessary parts or components from others or “cannibalize” i.e. remove parts or subsystems on some products for completing the assembly of others. If this happens, following the trail of missing and delayed parts upstreams can get tricky.

Combinations of V, A and T plants

V, A & T-plants are basic building blocks that can also be combined for more sophisticated categories. For instance a A base with a T on top, typical for consumer electronics. Yet the symptoms and remedies remain the same in each V, A & T category, combined or not.

Wrapping up

As we have seen so far along the 3 parts of this series, the search for the constraint in a system is more an investigation testing several assumption and checking facts before closing in on the culprit.

There are some general rules investigators can follow, like the search for large inventories in front of a resource while the downstream process is depleted of parts or material, but it is not always that obvious.

Knowledge about the V, A & T-plants can also help, without saving the pain of the investigation. And we are still not done in the search for the constraint! There is more to learn in the part 4!

Readers may be somewhat puzzled by my alternate use of the name bottleneck and constraint despite the clear distinction that is to be made between the two. This is because in the investigation stage, it’s not clear if the bottleneck is really the system’s constraint. Therefore, once identified, the critical resource is first qualified as a bottleneck and further investigations will decide if it qualifies for being the system constraint or not.

Bibliography about V, A & T-plants

For more information about V, A and T plants:

  • Try a query on “VAT plants” on the Internet
  • “Synchronous Manufacturing: Principles for World Class Excellence”, Umble and Srikanth, Spectrum Pub Co
  • “Theory of Constraints Handbook”, Cox and Schleier, Mc Graw Hill

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How to identify the constraint of a system? Part 2

When trying to find the system’s constraint, why not simply asking the middle management? At least when Theory of Constraint was young, our world spinning slower and processes simpler, the foremen usually had a common sense understanding of their bottleneck. They knew what machine to look after, and what part of their process to give more attention.

If you haven’t read part 1, catch up: How to identify the constraint of a system? Part 1

Even so they may not have discovered all 9 rules about managing a bottleneck by themselves, they intuitively applied some of them.

Nowadays the picture is blurred with complexity and frequent changes. Nevertheless, asking middle management can still give precious hints about bottlenecks. Ask what the more troublesome spot of the process is and from where / whom the downstream process steps are waiting for something. Chances are that many managers will point to the same direction.

This works for project management or (software) development as well. In those cases I would also ask who the superstar (programmer) is, the one everyone wants on his/her project and in every meeting. Chances are that that person turned into a constraint without even noticing it.

Now if the tip can be useful, refrain from rushing to conclusions from these answers and check for yourself. Many managers may tell you the same just because they all heard the same complaints in a meeting. A meeting where all managers meet…

Go to the gemba, look for Work In Progress

Let’s start the shop floor investigation searching for the bottleneck like it is described in the early text books.

Go to the gemba, follow the flow (which is easier and somewhat more natural than walking upstreams, but up to you to choose the preferred way) visually assess the work in progress (WIP) and inventories in front of the machines or work cells.

Usually the highest piles of Inventories or work in progress are sitting in front of the bottleneck and the following downstream process steps are starved from material or parts.

Yet if it would be that easy it would be no fun. The above works well in simple processes which are neat and tidy. Most often the inventories are scattered wherever it is possible to store something, FIFO (First In First Out) rules are violated and downstream processes, incentivized on productivity, work on whatever they can work on for the sake of good looking KPIs. Finding the bottleneck in such a chaos needs more than a visual check.

It is also possible that excess inventory and work in progress may be temporarily stored in remote warehouses and not in full sight, thus not visible.

Another pitfall is confusing work waves, periodically releasing parts or information, and real bottlenecks. An example could be a slow process which is not a true bottleneck but needs more than the regular shifts to catch up with its workload.

Imagine a slow machine (sM) amidst a process. The process upstream (P1) works 8 hours with best possible productivity and WIP piles up in front of sM. The downstream process (P2) works at best possible productivity and has some WIP in front of it.

At the end of the shift P1 and P2 are shut down. They both fulfilled their daily scheduled work. sM goes on for a second shift, processing the WIP in front of it.

By the end of the second shift, no more WIP (or very few) in front of sM and what was waiting in front of sM is now waiting after it, in front of P2. This is the picture the next early morning:

An observer, depending when he/she looked at the process, could have come to wrong conclusions about a bottleneck. Early morning it looks like the first machine of P2 is holding back the flow. In mid afternoon it is sM that is the culprit, when in reality there is no true bottleneck. sM has enough capacity provided it can work more than one shift.

Some would mention wandering bottlenecks, jumping from one place to another. This is something I will elaborate on in a separate post. Or series…

We are not done now with our bottleneck safari. To learn more, proceed to part 3.


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How to identify the constraint of a system? Part 1

A very common question once people get familiar with Theory of Constraints and the notion of bottlenecks and constraints is how to find them in a process. Identifying the constraint is key as the constraint, by its nature, controls the performance of the whole system.

The trouble with examples given in textbooks or case studies is that they are rather simple compared to finding the constraint in real life. This difficulty grew over time as processes got more complex, adding new layers of rules, standards and regulations. This complexity grew to an extend that many constraints remain elusive to people searching them, leading many people to be wrong when identifying “their” constraint.

Facing this kind of difficulties, readers asked me if a formal procedure to identify the constraint exists. I am not aware of such a procedure and from my experience the search for the constraint is much more like a detective’s job requiring investigation skills than applying a recipe. Some common patterns and similarities may exist, but every organization has some specificities that make the search for the constraint a special case. Therefore intuition and experience are definitely of great help.

In this series of post, I propose to review such investigations, that may help readers to transpose in their own situation, and eventually try to wrap up guidelines to identify constraints.

The usual suspects

First let’s review common bottleneck resources, keeping in mind that being a bottleneck is not synonymous of being a constraint and, as a general rule, a constraint is (and should be) a resource too long or too expensive to get more of it, or put differently turn it in a non-constraint.

A constraint was long said to be a very expensive piece of machinery or equipment which is too expensive as an investment to afford another one for additional capacity or which is not currently available.

Big stamping machines or presses, painting booth, heat treatment, surface treatment or sophisticated machine tools made good candidates for being bottlenecks and ultimately constraints.

Bad news is that things evolved, as we will see, and even if those bulky expensive or scarce equipment still make good candidates for the constraint status, they are not always the constraint.

For instance, I worked in a engineer-to-order company designing and manufacturing heavy mechanical equipment. The heat treatment was said to be the constraint and was managed by-the-book as a constraint.

After a short diagnostic, it turned out that heat treatment was not the constraint. It wasn’t because the true constraint was elsewhere in the process and those heat treatment operations could be subcontracted nearby at short notice and reasonable price. The subcontracting gave sprint capacity and provided relief whenever necessary. So heat treatment, even with long cycle times, was nothing really scarce nor excessively expensive.

Why was the heat treatment mistakenly thought to be the constraint? Because literature on the subject point this kind of process as usually being a bottleneck (remember: not enough capacity with regard to average demand placed on it). If indeed the workload often exceeded the capacity, the heat treatment was not the constraint. Failing to understand the difference between bottleneck and constraint led to a wrong conclusion.

Where was the real constraint? In engineering department where equipment are designed: people with specific skills that are long to learn.

More usual suspects

Very slow processes are usually also good candidates as bottlenecks: drying, curing, maturation, chemical or biological reactions, etc.

People with specific skills (as we have seen), knowledge, abilities, expertise, etc. that cannot easily be hired can also become constraints. So are some raw materials that are rare or dependent on harvest, climate, embargo, shortage, etc.

In some areas it is difficult to find some qualified workforce like welders, forklift drivers or specialists in some trade, which makes them constraints even so their profession is not so scarce at a larger scale.

When the constraint is not obvious nor easy to find, its identification becomes a matter of investigation. Investigation will start in part 2.


About the author, Chris HOHMANN

About the author, Chris HOHMANN

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What data for changeover monitoring and improvement?

CapacityMaximizing the exploitation of critical Capacity Constraint Resources (CCRs), so called bottlenecks, is crucial for maximizing revenue. Changeovers usually have a significant impact on productive capacity, reducing it with every new change made on those resources that already have too few of it.

Yet changeovers are a necessary evil, and the trend is going for more frequent, shorter production runs of different products, so called high mix / low volume. Consequently, changeovers must be kept as short as possible in order to avoid wasting the precious limited productive capacity of Capacity Constrained Resources (CCRs).

Monitoring changeover durations at bottlenecks is a means to:

  • reinforce management’s attention to the appropriate CCR management
  • analyze current ways of changing over
  • improving and reducing changeovers duration

Management’s obsession should be about maximizing Throughput of the constraints.

To learn more about this, read my post “If making money is your Goal, Throughput is your obsession”.

What data for changeover monitoring?

When starting to have a closer look at how capacity is lost during changeovers, the question is: besides direct periodic observations, what data are necessary and meaningful for such monitoring?

Before rushing into a data collecting craze, here are a few things to take into account:

In the era of big data, it is admitted now that one never has enough data. Yet data must be collected somewhere and possibly by someone. The pitfall here is to overburden operators with data collection at the expense of their normal tasks.

I remember a workshop manager so passionate with data analysis that he had his teams spent more time collecting data than to run their business.

Chances are that your data collection will be manual, by people on shop floor. Keep it as simple and as short as possible.

This a matter of respect for people and a way to insure data capture will be done properly and consistently. The more complicated and boring the chore, the more chances people will find ways to escape it.

Take time to think about the future use of data, which will give you hints about the kind of information you need to collect.

Don’t go for collecting everything. Essential fews are better than trivial many!

Be smart: don’t ask for data that can be computed from other data, e.g. the day of the week can be computed from the date, no need to capture it.

Example of data (collected and computed)

  • Line or machine number
  • Date (computed)
  • Week number (computed)
  • Changeover starting date and hour
  • Changeover ending date and hour
  • Changeover duration (computed)
  • Changeover type
  • Shift (team) id.

Explain why you need these data, what for and how long presumably you will ask for data capture. Make yourself a note to give data collectors regular feedback in order to keep people interested or at least informed about the use of the data.

Data relative to resources with significant excess productive capacity can be ignored for the sake of simplicity and avoid overburdening data collectors. Yet chances are that some day you’ll regret not having captured those data as well, and soon enough. Make your own mind about this.

Monitoring: what kind of surveys and analyses?

There are roughly two types of analyses you should be looking for: trends and correlations. Trends are timely evolutions and correlations are patterns involving several parameters.

Trends

One key trend to follow-up is changeover duration over time.

Monitoring by itself usually leads to some improvement, as nobody wants to take blame for poor performance i.e. excessive duration. As frequently things tend to improve spontaneously as soon as measurement is put in place, I use to say measurement is the first improvement step.

The first measurements set the crime scene, or original benchmark if you will. Progress will be appraised by comparing actual data against the original ones, and later the reference will shift to the best sustained performance.

In order to compare meaningful data, make sure the data sets are comparable. For instance certain changeovers may require additional specific tasks and operations. You may therefore have to define categories of changeovers, like “simple”, “complex”, “light”, “heavy”, etc.

Over time the trendline must show a steady decrease of changeover durations, as improvement efforts pay off. The trendline should fall quickly, then slow down and finally reach a plateau* as a result of improvements being increasingly difficult (and costly) to achieve, until a breakthrough opens new perspectives: a new tool, simplified tightenings, another organisation…

Changeover duration

*See my post Improving 50% is easy, improving 5% is difficult

>Consider SMED techniques to recover capacity

Correlations

Looking for correlations is looking for some patterns. Here are some examples of what to check:

Is there a more favorable or unfavorable day of the week? If yes, understanding the cause(s) behind this good or poor performance can lead to a solution to improve everyday performance.

Does one team outperform underperform? Is one team especially (un)successful? The successful team may have better practices than the lower performing ones. Can those be shared and standardized?

For instance if one team consistently outperforms, it could be this team found a way to better organize and control the changeover.

If it is the case, this good practice should be shared or even become the standard as it proved more efficient.

I happen to see the performance data from a night shift in a pharma plant being significantly better than the day shifts. Fewer disturbances during the night was the alleged cause.

Be critical: an outstanding team may “cut corners” to save time. Make sure that all mandatory operations are executed. Bad habits or bad practices should be eradicated.

Conversely, poor performing teams may need to be retrained and/or need coaching.

Is one type of changeover more difficult to master? Search for causes and influencing factors. Some engineering may be required to help improving.

These are only some examples of patterns that can be checked. Take time to consider what factor can have some influence on changeover ease and speed, than check how to test it with data and how to collect these data.

Note that correlation is not causation. When finding a pattern, check in depth to validate or invalidate your assumptions!

Speak with data

All the data collection and analysing is meant to allow you and your teams to speak with data, conduct experiments in a scientific way and ultimately base your decisions on facts, not beliefs or vague intuitions.


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If making money is your goal, throughput is your obsession

In a for-profit organization making money is the goal and the limitation to making more money is called a constraint.

Conversely, a constraint is a limiting factor to get more out of the system. There is only one constraint which is the most limiting factor restricting the Throughput.

Throughput is the rate at which the organization is making money.

If the constraint is limiting Throughput, it means the constraint controls all the money-making.

From this point, making the maximum money given the constraint, there are two (cumulative) options:

  • Elevate the constraint, which means get over the limitation of the constraint to allow more Throughput.
  • Keep Throughput at its maximum by avoiding anything limiting it more.

Elevating the constraint might be difficult or even impossible to do, simply because if it wouldn’t, chances are it would already have been done. More seriously a constraint can be something very difficult to get or to change, like a very expensive equipment, something very rare or something very difficult to influence/change like regulation or policy.

Keeping Throughput at maximum in the given conditions is called exploiting the constraint. It requires constant attention to prevent anything to choke the Throughput.

That’s why once the constraint is identified, it becomes the center of all attention. If the constraint is a resource, like a machine, an equipment, a department or some talented person, this resource deserves a special treatment to protect it against anything limiting its Throughput further.

As the constraint controls all the money-making, it is a good spot where to literally sit and constantly monitor the Throughput. Every decision should be made with regards to its influence to the Throughput:

  • if it is reducing the Throughput, it must be challenged
  • If it is increasing or a least securing the Throughput without adding more Operational Expenses (Net Profit = Throughput – Operational Expenses), it must be considered.

Therefore, if making money is your goal, Throughput is your obsession.


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Your next bottleneck is elsewhere (and in the future)

Theory of Constraints provides the five focusing steps, an iterative improvement process which aims to focus efforts on the sole system constraint (often a bottleneck).

These five steps are:

  1. Identify the constraint (bottleneck)
  2. Exploit the constraint; improve capacity utilization
  3. Subordinate all non-constraint resources to the constraint
  4. Increase the capacity of constraint if relevant
  5. Repeat step 1 if the constraint has changed

The final step is an invitation to continuous improvement, but also a warning: do not let inertia, passivity and acceptance of the status quo become the constraint.

Yet one other aspect of this warning remains mostly unknown.

While teams work hard to exploit the bottleneck resource and recover some wasted capacity, they do not anticipate that the next bottleneck is located elsewhere in the future.

Most teams working to elevate a capacity constraint do not imagine that the additional capacity that will be recovered requires immediate anticipated loading.

Indeed, most of the time, once the goal is reached and the bottleneck is no longer the constraint, they “expect” to see another bottleneck emerge in their area, as if they were playing whack-a-mole; hit one, wait for the next to pop-up.

Chance are that the next bottleneck will probably not be found within their perimeter. The next bottleneck can be upstreams, in another department or with some supplier.

The next bottleneck will instead most likely be found either in development, engineering, marketing or sales. And it will come as a surprise due to lack of anticipation.

The next bottleneck may be the order book, as sales nor marketing did not anticipate the loading of the recovered capacity. It may be development, unable to bring forward the launch of the next product.

It lies in the future is a warning about the necessity to anticipate it and the probable time lag before the anticipated efforts pay off.


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Constraint vs. bottleneck

In Theory of Constraints lingo, there is a subtle difference between a constraint and a bottleneck.

A bottleneck (resource) is a resource with capacity less or equal to demand while a constraint is a limiting factor to organization’s performance, an obstacle to the organization achieving its goal.

A constraint can be called bottleneck but a bottleneck is not always a constraint.

Let’s take an example of a plant with a subassembly workshop gathering resources A, B and C. The whole process needs another resource D and final assembly consisting of resources E and F. The capacity of each resource is displayed under their letter.

The demand is 100 units per day.

According to definitions we’ll find two bottlenecks: resource B limited to 80 units/day and resource E limited to 60 units/day. Each of these two have a capacity less than daily demand.

Resource B is handicap to resource C and for the whole subassembly workshop, but has little influence on the throughput of the plant. Plant’s throughput is limited by resource E, which is both a bottleneck and the constraint. It is primarily E which hinders the plant to deliver 100 units/day.

Imagine The subassembly is led by a foreman named Hector. Hector’s realm encompasses The resources A,B and C. The final assembly process is his customer.

Hector has significant experience within this company and is well aware B is a bottleneck. Even so Hector may not know anything about Theory of Constraints, his common sense made him discover some good rules to better exploit the bottleneck resource.

For example, Hector organized breaks so that B is never left unmanned and not running, manages to minimize changeovers.

If he knew about Theory of Constraints, he would probably squeeze more throughput from B, for instance placing the quality check before the bottleneck in order to insure only OK parts will be processed by the very limited B. Actually quality check is after C, which sometimes causes B to waste valuable time processing parts that will not pass the quality check, something that could be foreseen before B.

As it is the case in many companies, top management set local productivity objectives and is expecting Hector’s subassembly to run with best productivity. Logically Hector will complain about B’s limitations and keep asking for another investment in a second B. Waiting for this investment, Hector manages to produce daily around 80 units, the best subassembly can do.

In Hector’s eyes B is the constraint, which is true if we consider subassembly alone.

Production manager Isadora has to take care about the whole plant and thus considers the whole process. She doesn’t know either about Theory of Constraints, but her analytical skills and common sense focused her attention onto E, the bottleneck and constraint to the whole process.

Having limited means, she’ll explain Hector that working to increase the capacity of B would have little interest as long as E is the limiting factor for the whole system (the plant). What Isodora did not notice is that as long the daily limit is 60 units/day, some costs could be saved in subassembly if its daily production would be aligned to the capacity of E, for instance overtime and excess inventory carry over costs. But she’s blinded by local productivity objectives set by top management.

Nevertheless, Isadora came close to self-discover the five focusing steps of Theory of Constraints:

  1. Identify the constraint (E)
  2. Exploit the constraint
  3. Subordinate everything to the constraint (e.g. subassembly)
  4. Elevate the constraint
  5. Prevent inertia to become the constraint

If Isadora succeeds to elevate the constraint E, chances are that the B will be the next constraint!


Related:


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