How to identify the constraint of a system? Part 5

This is part 5 of a series of posts about identifying the constraint of a system and time for wrapping up and a conclusion (of the series, not the topic!).

Newcomers to Theory of Constraints understand quite easily the concept of bottleneck but are frequently puzzled when looking for them in a real-life process. Furthermore, people have usually difficulties to distinguish bottlenecks or Capacity Constraint Resources from the system’s constraint. As only the latter controls the Throughput of the whole system, meaning it does control the system’s profitability, constraint hunters should better not mismatch the focusing point.

Through the examples in the previous posts, readers can understand that there is no such a thing like a simple trick, neither ready procedure to find the constraint, but a necessary and sometimes painstaking investigation process.

If you missed previous posts, you may check:

Some constraints can really be well hidden and remain elusive, leading analysts to be wrong when identifying it. Because of the growing number of standard and regulatory requirements, the constraint is more and more often found in the paperwork process, in quality assurance or information flow. And this is the wrong place for the constraint to be.

How to be sure the constraint is found?

With so many opportunities to mismatch the constraint, how can one be sure about having found the constraint? Well, by definition the constraint controls Throughput. Elevating the constraint is like opening a valve, the flow through the constraint increases. And as, also by definition, all other resources have excess capacity compared to the constraint, the flow should soon reach the process’ output. This enables the organization to ship more, to ship on time, take more orders and shorten time-to-cash.

For non-profit organizations, the Throughput is expressed in “Goal units”, meaning achieve more of what the organization exists for, like treating more patients for a medical center, providing more food to the homeless, etc.

Now this is quickly noticeable if the downstream process steps are waiting for supplies from the constraint, otherwise the flow from the constraint may take longer to reach the process’ end.

Be aware of the S curve and Valley of despair

As with many improvement initiative, the result may be delayed to a point that some stakeholders come to the conclusion it doesn’t work. Before getting trapped in this pitfall, the project manager or the leader should be aware of the frustration with the S curve.
When dealing with bigger projects rather than improvement activities, it’s not only the S curve the project manager and the sponsors should worry about, but also the “Valley of despair”. This valley is a low in morale following the excitement and expectations about the benefits that the new project will bring. The drop in morale comes when issues and bugs let the new solution appear worse than the old. The challenge for the leader is to get everyone as quick as possible through the valley of despair, accommodate the new way or system and eventually recognize the benefits.

Keep on alert once the constraint is found!

Now once the constraint is properly identified and the efforts to exploit and elevate it begin, the leaders should immediately take care of the consequences of releasing more “goal units” through the constraint.

  • First because the constraint will most probably move to another spot and again this can be anywhere in the process.
  • Second because upstream as well as downstream process steps may be taken by surprise. Upstreams by an increase of demand in order to supply and exploit the increased capacity. Downstream by the flushing of the work in progress that may propagate a significant wave of workload.
  • Third because management must anticipate any necessary action to sustain the flow at the new level, otherwise the success will only be sporadic and ultimately disappointing.

I recommend reading two other posts related to these warnings:

Conclusion

The search for the system’s constraint can be very simple and straightforward, but most often it is tricky and leads to identify wrongly some resources as culprits. It is no rocket science but needs investigation skills and rigor. Experience helps a lot and the good news is that taking care of constraints is never-ending, so experience may accumulate fast.

About the author, Chris HOHMANN

About the author, Chris HOHMANN

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How to identify the constraint of a system? Part 4

Since the publishing of early books on Theory of Constraints, the world grew more complex and the system’s constraint got more and more elusive. Globalization and extended supply chains give a constraint opportunity to settle literally anywhere in the world and extend its nature. It can be a physical transformation process in a supplier’s facility, it can be the way cargo is shipped from distant suppliers to the company, it can be the custom clearance process somewhere along the supply chain.

Walking a factory door to door may not suffice anymore to find the system’s constraint. The examples given in the part 1, 2 and 3 of this series of posts are simplified with regard to the reality of most companies.

Another complexity is brought by the growing number of requirements of standards and regulations. A company wanting to count among the aeronautical industry makers has to comply to the AS 9100 (USA) / EN 9100 (Europe) / JISQ 9100 (Asia) standard. For the automotive industry the standard to comply to is ISO/TS 16949 (now IATF 16949). And those two examples are only standards for the quality management system.

Pharmaceutical industry, as some others, require a license to operate. In order to be awarded such a license and to keep it, the company must comply to all requirements, undergo periodic audits and keep record of anything happening along the manufacturing process. This industry is under constant scrutiny of government agencies, regulators, etc.

Therefore, the paperwork associated with products is impressive and requires a lot of resources in the dedicated processes, and as we will see, likely to host a system’s constraint!

Over time, layers of requirements accumulated. And what is a requirement if not a limitation of the way to execute, a constraint?

Quality assurance

Quality assurance (QA), according to wikipedia, comprises administrative and procedural activities implemented in a quality system so that requirements and goals for a product, service or activity will be fulfilled. It is the systematic measurement, comparison with a standard, monitoring of processes and an associated feedback loop that confers error prevention. This can be contrasted with quality control, which is focused on process output.

https://en.wikipedia.org/wiki/Quality_assurance

Anyone working with a Quality Assurance department soon realises that this department is more acting as a defense attorney for the company against regulatory or standardization agencies, and a watchdog internally than a support for improving quality by problem solving.

For obvious reasons, QA and Production must have a clear divide, as it would not be acceptable for the maker to assess and certify the quality of his own production. Their staff are also distinct. QA usually has a huge influence on decisions and can be very powerful, to the point that top executives have to accept QA decisions, especially when QA has to sign off the release of a batch or clear the allowance to ship.

QA activities are mainly administrative, with some lab testing. QA staff is “white collar”, working a typical 9 to 5, 5 days a week regardless of production. Some QA authorizations are mandatory for the physical batch to move to the next step in the process. Many productions run more than one shift, up to 24/7, while QA works 1 shift 5 days a week. As a result, the paperwork relative to production batches accumulate during the QA off-period and is later flushed during QA working time.

Now here comes the first problem. The difference of working time patterns send waves of workload through the system. It is not uncommon for some production batches to wait for QA clearance in front of a process or in a warehouse. This could give the impression that the bottleneck is in the next manufacturing processing step, but it is not.

In reality the bottleneck is in QA. It can be the plain process of reviewing of paperwork or some testing, measurement, analyses, etc. A trivial yet common bottleneck is the “qualified person”, the one or few ones entitled to sign off the documents. Those people, usually managers, are busy in meetings and other work and let the paperwork wait for them.

Note that QA activities are not always extensively described in the production task lists, do not always have allocated time and if they have, QA department is seldom challenged about the staff adhering to standard time neither to possibly reduce the duration by some improvements. This can lead to underestimate the impact of QA’s activities on the production lead time and “forget” to investigate this subject when searching for the bottleneck.

Dependence on third parties

With an ever growing number of requirements to fulfill and proofs, certificates and log files to keep ready in case of inspection, many specialized tests and measurements are farmed out to third parties. It makes sense, in particular if those activities are sporadic, the test equipment expensive and maintenance of skills and qualification for personnel mandatory.

Now this type of subcontracting bears the same risks than any other subcontracting: supplier’s reliability, capability, capacity, responsiveness, etc. and the relative loss of control of the flow as it is now dependent on a distinct organization. The system’s constraint may well be located then outside of the organization, and even beyond its sphere of influence!

Beware of the feeling of being in control when the third party operates in-house. I remember such a case where a specialized agency was doing penetrant inspection and magnetic crack detection in the company. While everything seemed under control, the external experts often failed to come as scheduled because they still were busy elsewhere or had sick leave. When they were in-house, they frequently lost a fair amount of their precious time moving parts around, a kind of activity not requiring their qualification but significantly reducing their availability for high-value added tasks. It turned out that this spot in the factory often was a bottleneck due to the lack of management’s attention.

Where Value Stream Mapping can help finding the constraint

These examples above show that the information flow or paperwork associated to the physical flow can have a significant influence on lead time and can even decide if the flow has to stop.

In such cases Value Stream Mapping (VSM) can help finding the constraint as it describes both physical and information flows on a single map. Note that some companies including Toyota refer to VSM as MIFA, the acronym of Material and Information Flow Analysis.

Without such a map to guide the investigations, people on shop floor may forget to mention (or are not even aware of) analyses, tests, approvals, paperwork review, etc. during interviews of gemba walks. Experienced practitioners will ask about these possibilities when inquiring in strong standard or regulation-constraint environments.

Where the Logical Thinking Process can help

When the system’s constraint remains elusive despite all the search with previously mentioned means, Theory of Constraints’ Thinking Processes or the Logical Thinking Process variant can help finding the culprit by analyzing the Undesirable Effects at system level.

This later approach is best suited for “complex problems” when the constraint is a managerial matter, conflicting objectives, inadequate policies, outdated rules or false assumptions, myths and beliefs.

To learn more about the Logical Thinking Process and the logic tools, see my dedicated pages, series and posts on this blog.

About the Author, Chris HOHMANN

About the Author, Chris HOHMANN

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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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My blog’s third birthday

January 2014 – January 2017, my blog is now online for 3 years and counts 347 posts.

Thanks to all of you my audience is gently growing on this blog, as well as on my Youtube channel and on tweeter. All organic!

What is the most read here?

According to the stats, Constraint vs. bottleneck is the absolute winner, ahead of 3D Printing and Porter’s five forces ranking second.

Then comes a string of posts related to the Logical Thinking Process and the popular Goal Tree.

What’s on schedule for 2017?

Well I have a huge inventory of titles, topics, half-written posts on the various subjects I’d like to share: Lean Management, more about Logical Thinking Process and Theory of Constraints, my prospective survey about the future of manufacturing and much more.

I’ll try to post on a regular basis and bring some value-added content. You are welcome to give me feedback in the comments.

Hope to see you here!

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When enough is… enough

cho-in-azoneThis is a behavior I’ve noticed quite often in food industry, in chemical or pharmaceutical plants: cleaning and sanitation processes (mainly their duration) are extended beyond the standard procedures at the expense of costs and production capacity.

Fear of harming

In the regulatory-constraint industries like food, chemical or pharma, people on shopfloor are trained and qualified to perform cleaning and sanitation operations. They follow procedures and work instructions, based on standards.

They usually also have frequent training about the importance of sanitation or sterilization and the possible consequences if badly done. Working in food, healthcare or pharma is embracing the sacred mission to bring something good, to cure or relieve customers and/or patients and do everything to prevent hurting them in any way.

They are also reminded what consequences for the organization in case of problem beyond failing to: losing the customers’/patients trust, losing the licence to produce, being sued, being exposed to scandals…scary enough for shopfloor people to take things seriously.

Yet the people on shopfloor seldom have the scientific background to fully understand what is required for good sanitation or sterilization, when doing more is useless or even counterproductive. They also are often left on their own, without expert supervisors to reassure them, answer possible question or take decisions in case of doubt.

Furthermore, the results of sanitation/sterilization is most often only known after a sample of rinsing water or the swabbing of the tool/equipment has been analyzed by some remote lab.

Fearing to harm the organization, or worse the customers / patients or possibly to have to go over the whole lengthy sanitation process again if it is not satisfactory, the sanitation is performed longer than procedures require it. This is base on the belief the more the better.

This seemingly logical and well-intentioned assumption is never challenged, leading to waste detergents, acids, water… and time, simply because over-sanitation is not noticed by management.

Changeovers are even longer

Changeovers in such environments can be long and painstaking due to regulatory constraints and all the paperwork associated. Ignoring the over-sanitation habits can extend the changeover duration even more.

Besides adding costs for no additional value, the additional time spent on sanitation may be needed on critical equipment (bottlenecks) and the time lost will not only never be recovered but the true cost is to be counted in minutes of turnover. And this one can be skyrocketing!

Conclusion

When looking for additional productive capacity or a way to get more out of the current process, check the changeovers’ content and take a closer look on sanitation.

Give the shopfloor personnel clear indication when enough is enough, without risk to harm anybody nor to endanger quality. If necessary, have a real qualified subject matter expert attending these critical phases, ready to support the team and answer any question.

Not only will it take some concerns off the team, but may be a great payback in terms of additional yield.


Feel free to share your thoughts and experience in the comments and share the post if you liked it.

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My takeaways from throughput accounting, the book

I knew the author, Steven M. Bragg from his podcast series “Accounting Best Practices with Steven Bragg” before I came across his book “throughput accounting, a guide to constraint management” published by Wiley & sons, 2007.

Book presentation

The hard cover book has 178 pages, 10 chapters, easy to read in neat presentation and legible fonts, with numerous tables, graphs and illustrations to back up all the provided examples and case studies.
It claims to contain the tools needed to improve companies performance for accountants, financial analysts, production planners or production managers.

>Lisez-moi en français

The book starts head on by introducing the basics of Theory of Constraints (ToC) in an uncommon, and for me daring way: explaining very briefly the Drum-Buffer-Rope (DBR) logic, in chapter 1 (page 1!).

It is daring because it’s a shortcut putting DBR upfront when it’s usually presented to newbies (long) after explaining the bottleneck concept and the differences between traditional manufacturing, trying to run every resource at full utilisation rate, versus the ToC approach where “only” the bottleneck matters (this is another shortcut, but of mine…).

It goes on with presentation about the different types of constraints, not all being bottlenecks, discussing the nature of the constraint (page 5). The Throughput Accounting (TA) KPIs are presented page 7 and 8 before diving into the financial aspects of TA.

Chapter 2 is about Constraint Management in the factory, starting with how to locate the constraint and how to manage the constrained resource. The various hints are clearly targeting managers or readers keeping some distance from shopfloor as they give enough insight without being too detailed. No people will go through and get bored, the various hints are condensed within few lines, without giving up anything important.

Four pages deal with policy constraints, again something of interest for managers and readers that may have influence within their own organization to educate their colleagues about the drawbacks of some policies and hopefully change them. The importance of constraint buffer comes page 25 followed by the importance of proper batch sizes and machine setups.

Chapter 3 is about throughput (T) and traditional cost accounting concepts and starts with the emphasis on cost versus Throughput and goes on with all the consequences describing why traditional cost accounting – companies that means – is suffering from several problems.

This chapter is important for people not very familiar with accounting, especially in operations, because it explains some of the decisions that make no big sense when considered from operations point of view. It is also important for those familiar with traditional cost accounting for to understand the limitations and problems brought up by that approach.

Chapter 4 is about Throughput and Financial Analysis Scenarios and from page 59 to 86 take the readers through 14 different scenarios, from Low Price, High Volume Decision to Plant Closing Decision.

Chapter 5 is on Throughput in the Budgeting and Capital Budgeting Process, chapter 6 about  Throughput and Generally Accepted Accounting Principles and chapter 7 about Throughput and Control Systems.

Chapter 8 details Throughput and Performance Measurement and Reporting Systems, interesting because it links the operations’ reality to usable KPIs, e.g.

  • Ratio of Throughput to Constraint Time Consumption
  • Total Throughput Dollars Quoted in the Period
  • Constraint Utilization
  • Constraint Schedule Attainment
  • Manufacturing Productivity
  • Manufacturing Effectiveness
  • Order Cycle Time
  • Throughput Shipping Delay
    And more.

Chapter 9 is named Throughput and Accounting Management and addresses 12 decision areas among which: Throughput Analysis Priorities, The Inventory Build Concept, Investment Analysis, Price Formulation.

Finally chapter 10 presents 7 Throughput Case Studies each of them in a couple of pages.

My takeaways

The book is easy to read and to all concepts are easy to understand thanks to the simple ways the author puts them. Not being an accounting specialist at all, I always liked the simple, pragmatic and concise ways Steven Bragg explains accounting rules or practices. This book is not different.

Reading “throughput accounting, a guide to constraint management” reinforced both my knowledge and my interest in throughput accounting, as well as the conviction about throughput accounting being a powerful and crucial decision-making approach.

I’ve marked dozens of pages with sticky notes highlighting my points of interest and/or inspirations for posts on my blog, reinforcing my consulting approach, etc.

Throughput accounting

Almost all companies have their management heavily influenced by traditional cost accounting and most of them make ill-oriented decisions. With the book’s content help, it is easier to explain to CFOs and CEOs why their decisions are biased by false assumptions or outdated rules, something that can be quite shocking to them.

The book doesn’t come cheap, but as it explains, quit reasoning in terms of cost savings and consider how much (intellectual?) Throughput it can leverage.


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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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Schragenheim’s concise history of constraints

The definition of a constraint in Theory of Constraints (TOC) has varied as the corpus grew and matured. Still today it is confusing for newbies to sort out what is meant with “constraint”, depending how they got their basics in TOC.

Thanks to Eli Schragenheim, one of TOC’s founding fathers, and the related post on his blog, the reader can understand how and why the definition varied over time.

I strongly recommend to read Eli’s post: A concise history of constraints