What it takes to achieve your objective: Means, Method and Motivation

When facing a challenge, many subordinates quickly complain about the lack of means and  scarce resources. But what it takes to achieve an objective is more than just means.

The common complaints about means

Complaining about scarcity of means or resources is a convenient way to push the responsibility of the difficulty of the challenge and the possible failure to the boss, and often comes without any evidence for the resources really being scarce.

You may like my post Do what you can, with what you’ve got, where you are

Time allocated to achieve something can be considered as a means. If the management gives no time or too few time for some tasks but expect a result nevertheless, the complaint about not having enough time can be legit.

This is the case with daily machines cleaning  (Autonomous Maintenance) after the shift is over, allocating some time to stop production and clean, lubricate and visually inspect the machines for the sake of failure prevention. But if production is late and management orders to work till the very last minute, there is no time left for the daily basic maintenance. If this is repeating over and over, the machines’ condition may decline and a serious failure stop them for a longer period.

Some means are absolute prerequisites to achieve the objective and if they are not granted, the objective may be out of reach, despite the alternative or creative ways people try.

Means are also often confused with method or a process, and people complaining about insufficient means are in reality worrying about how to proceed. Having the appropriate means is then synonymous to knowing how to do, with the hope that this or that tool, machine, equipment, etc. will bring the solution with it.

The triple prerequisite to achieve an objective

Besides appropriate means, the method or way to proceed is another prerequisite. If you get all the parts necessary to assemble a computer but have no idea how to assemble them, you have all the necessary means but are still unable to have a working computer. Therefore, the second prerequisite, besides means, is method or know-how or knowledge, procedure, instructions, etc.

There is a third prerequisite to achieve an objective: motivation. If you have all necessary means and have necessary know-how and skills but no motivation (or will, desire), the objective will not be achieved.

The triple requirement for achieving an objective is to have appropriate Means, Method and Motivation, which can be memorized as 3Ms.

Now when assigning tasks or objectives to teams, managers better make sure the means are provided, the know-how or skills are available and check the motivation.

This post was inspired by an explanation of my friend and mentor Bill Dettmer during a Logical Thinking Process training course. You may listen and watch Bill’s explanation in the video beneath. To understand the context you may read my post Goal Tree Chronicles – Enablers vs.triggers.


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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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Goal Tree Chronicles – Enablers vs.triggers

In this post I explain the difference between enablers and triggers in logic trees, which basically is explaining how Necessity logic differs from Sufficiency logic. I then explain the basic assumption when building a Goal Tree and why the Goal will not automatically be achieved even if a most of Necessary Conditions are fulfilled.

Necessity vs. sufficiency

Necessity-based logic requires a prerequisite to be fulfilled in order to produce the expected effect. This is why necessity-based logic uses “in order to… [effect] we must … [prerequisite]” wording in the Logical Thinking Process.

Example: in order to have my hair cut, I must go to the hairdresser.

Even so there are alternatives to the hairdresser to have the hair cut, a prerequisite is necessary for the hair being cut.

Sufficiency, as its name suggests, does only require the cause to exist for the effect to automatically exist. The corresponding wording is ”if…[cause] then.. [effect].”

Example: if it rains, then the lawn gets wet. Or if I drop an ice-cube in hot water (the) it melts. In these examples there is little that can be done to prevent the effect to automatically happen when the cause happens.

Enablers vs.triggers

I assume dear readers, you understand the huge difference between Necessity and Sufficiency. While an effect will automatically happen if the cause exists in the case of sufficiency, the existence of the prerequisite (cause) in necessity-based logic is not enough to produce the effect, it only enables it.

For example, many prerequisites are necessary to build a house, like having a ground, having timber, having a permit, and so on. But having all prerequisite will not lead the house to build itself.

In sufficiency logic, the cause is the trigger while with necessity logic, the cause is “only” an enabler.

The Goal Tree is built on necessity-logic

The Goal Tree, one of my favorite logic tools, is built on layers of Necessary Conditions, linked from the Goal on the top to the very first Necessary Conditions at the bottom by necessity-logic. The convenient way to build a Goal Tree and scrutinize it is to check the sound logical relationship between an entity and the underlying Necessary Condition using the “in order to… [effect] we must … [prerequisite]” phrasing.

The logic trees and cloud from the Logical Thinking Process are either necessity-based or sufficiency-based and in the order of their sequential usage they alternate between necessity and sufficiency.

Now because the Goal Tree is built on necessity logic, the entities composing it are absolutely necessary to exist or being granted for to achieve the Goal. By definition, if one Necessary Condition is not fulfilled, the Goal cannot be achieved.

But, as Necessary Conditions are “only” enablers, nothing will happen as long as no real action is taken.

Achieving the Goal

Achieving the Goal requires all Necessary Conditions or enabling prerequisites to be fulfilled, but it is not sufficient.

This can be disturbing for those being exposed first time to the Goal Tree, because there is an implicit assumption that when the enablers are in place, the necessary actions or decisions will be taken, so that from bottom to top, all Necessary Conditions are fulfilled and the Goal eventually achieved.

Promoters, including me, tend to cut corners and advertise about the lower level Necessary Conditions “automatically” turn the upper ones to be fulfilled, and the achievement of intermediate objectives to happen like a row of dominoes propagating the fall of the first one till the very last: the system’s goal.

This is true if people in charge do their part: take the decisions and/or carry out the tasks.

This is why, “surprisingly”, some entities can be Amber or Red (condition not always / not fulfilled) even so their underlying Necessary Conditions are Green (condition always fulfilled).

If you are not yet familiar with my 3-color system, I suggest you read: 3-color system for Goal Trees

Example

Here is such an example. It comes from an operational Goal Tree built to enumerate all Necessary Conditions to pass over simple maintenance tasks from maintenance technicians to line operators. The simple tasks include daily lubrication and check of tightenings in order to prevent wear and possible breakdowns. The aim is to implement the Total Productive Maintenance ‘Autonomous Maintenance‘ pillar.

Once all Necessary Conditions are listed, the Goal Tree is scrutinized for robustness and if ok, it becomes the benchmark to achieve the Goal. The next step is to assess each Necessary Conditions for its status.


We see in the figure above (showing only a tiny part of the Goal Tree) that all underlying Necessary Conditions to “Daily lubrication / tightening is done” are Green, but the expected outcome, the effect is Amber. Since every prerequisite is Green, we expect the effect to be Green as well. Amber means the outcome is not stable, not always guaranteed, not steadily at nominal level.

It means this expected outcome, the task “daily lubrication / tightening is done”  is NOT done EVERY day.

One may argue that we cannot see any mention of the lubrication / tightening being part of operators’ duties. That’s correct. The reason for this is that in logic trees, obvious prerequisites or assumptions are voluntarily omitted for the sake of keeping the logic trees simple and legible. In our case, the work instructions include the daily lubrication and tightening routine. This is a known fact for everyone concerned with this Goal Tree.

In other words, enablers are ok, but the trigger is still missing.

It is now up to management to:

  • make sure operators have a full understanding of the work instructions,
  • make sure these tasks are carried out and
  • clarify what is to be done if operators face a dilemma like catch up late work or go as planned for maintenance routine.

Fortunately those cases are the exception. People truly involved in a project and having a clear understanding of the purpose will contribute. That is, as long as they are not exposed to undesirable effects, from their point of view.


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Factory of the future is a misnomer

There is a real hype around the “of the future” nowadays (we write November 2017) in France. Everything seems to be “of the future” and it started with the factories supposed to soon buzz with the sound of toiling robots and frantic printing 3D printers.

“of the future” sounds great, full of promises of extraordinary technologies and unbelievable possibilities. A kind of science fiction world, full of flying cars by the year 2000, as we were told in my childhood…

>Lisez-moi en français

What bothers me is that the described factories of the future and their promises are based on  already available technologies. So what is left “of the future” then?

The “factory of the future” was probably an answer to the German “Industry 4.0”. As usual the national pride did not allow to rally a foreign initiative and prefers to reinvent the whole thing and rebranding it.

By naming the concept “factory of the future”, I fear that many decision makers understand that the technologies are not fully ready yet, that it’s still a concept for research and it will take a while until everything is mature and affordable for the medium-sized companies to pay closer attention.

What leaves the new manufacturing ways and the factory in the future is the postponed decision to go for it. I repeat: the necessary technologies are already available.

This false feeling of having time to consider and decide could have dire consequences, the risk of being disrupted by a more daring competitor is more likely for tomorrow morning than later in time.

As nice and promising as it sounds,  “factory of the future” seems to me an ambiguous misnomer.
Comments welcome.

Author Chris HOHMANN

Author Chris HOHMANN

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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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You are entering unclear territory

This post is inspired by some pictures* in Mike Rother’s book “Toyota Kata”, very relevant in our actual changing times and complex situations. I really like the figures that remind that the journey towards a goal requires entering unclear territory at some point.

Unclear territory

Adapted from “Toyota Kata”, Mike Rother

Experience from the military tells that no plan, regardless how carefully it was planned, will survive contact with the enemy.

No plan survives contact with the enemy.
Helmuth von Moltke
https://en.wikiquote.org/wiki/Helmuth_von_Moltke_the_Elder

What this means is that unknown facts, unexpected events and dynamic complex interrelated relationships will interfere with the plan based on partial knowledge, biased perception and simplified hypotheses.

Plans are important because they allow to explore hypotheses and be prepared accordingly but most plans are useless as reality often unfolds differently as expected.

This is why, when starting a journey toward a significant change or a Lean transformation for example, the plan will probably last only for the few first steps, but barely further. Once the journey started, unexpected circumstances will reveal themselves and require adjustment.

This is similar to exploring a dark room with a flashlight**. Only a part of the room is visible and every move is done accordingly to visibility and noticed obstacles. As one moves forward the line of sight extends and the vision clears further one step at a time. New obstacles appear, requiring new adjustment, and so on. The path across the dark room is probably not straight, but must get past several obstacles.

The same happens while hiking. The goal is set, the map shows the terrain and the compass helps for the bearing. Yet the map may show the canyon, the river and the forest, but none of the boulders, the fallen trees across the path neither the recently flooded areas. Those obstacles will appear once the hikers come close. In this case too, the hikers have to adjust to circumstances and find alternate routes to reach their goal. The one they had in mind while planning the hike is no more relevant.

Leaders must be able to lead through unclear territories

Unclear territories of all sorts are more and more common. Even what we believed familiar ground can be disrupted overnight. Everything is going “VUCA”, meaning being increasingly Volatile, Uncertain, Complex and Ambiguous. More decisions must be taken, more frequently and often with incomplete data and only partial understanding of the situation.

Among all decisions, some – and hopefully few – will show unadapted, requiring new adjustments. Wandering in unclear territories is a new fate. Being able to make quick and good decisions in unclear territories is a necessary aptitude for aspiring leaders.

What are good decisions? Those enabling the organization to achieve its goal despite the obstacles and unexpected difficulties, those solving the problems to clear the way towards the goal or choosing alternate routes to get closer to the goal.
Therefore, having a clear Goal onto which aligning actions, projects and initiatives when going through unclear territory is mandatory.

Followers too must understand “unclear territories”

Unclear territory is not a concept for leaders only, the followers must understand it too. What does it mean? It means that once entering unclear territories:

  • leaders (managers, people in charge…) may get surprised by unexpected events and this does not make them bad leaders
  • leaders, managers, etc. don’t have all the answers to all difficulties and problems that arise along the journey
  • plans and projects sometimes have to be reoriented, which may look like poor management or a fantasy but isn’t
  • some invested efforts and actions must be abandoned due to new circumstances, it’s disappointing, but that’s life

One may wonder how to distinguish poor leadership from the necessities to adapt to new circumstances? I would suggest to prevent the question from arising by establishing a clear communication upfront, giving frequent and transparent updates and have everyone exploring the unclear territory by teamwork.


*Mike Rother “Toyota Kata: Managing People For Improvement, Adaptiveness, and Superior Results”, McGraw-Hill Professional, 2010. Pictures mentioned are page 8; 120; 124; 133; 163

**Flashlight metaphor, page 133


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What is autonomous maintenance (TPM)?

Autonomous maintenance is one of the 8 Total Productive Maintenance (TPM) pillars, it aims to give both competence and responsibility for routine maintenance, such as cleaning, lubricating, and inspection to operators.

The aims and targeted benefits of autonomous maintenance

The ultimate goal of Total Productive Maintenance is to enhance machines’ effectiveness. TPM is a participative approach, involving all stakeholders and taking into account all aspects of maintenance. In order to achieve this goal, TPM is split in 8 pillars or topics. Autonomous maintenance is one of the 8 and is about simple mundane tasks, but having their importance nevertheless. The expected outcomes are:

  • Operators’ greater “ownership” of their equipment
  • Increased operators’ knowledge of their equipment
  • Ensuring equipment is well-cleaned and lubricated
  • Identification of emergent issues before they become serious failures
  • Freeing maintenance personnel for higher-level tasks

Operators’ ownership

Operator’s ownership of their equipment is meant to close the divide between Production and Maintenance in cases where the first claim “my job is to produce” and the second “my job is to repair”. This is mainly the case when production staff is incentivized on production output and maintenance is jealous about keeping its technical skills and prerogatives.

What happens then is that production operators do not usually care much about the equipment and machines they use and are prone to trespass speed limits, for example.

As they are not supposed to do anything about the machine breaking down, they soon find out that breakdowns are opportunities for an extra break, hence an extra smoke, one more coffee and so on.

As a result, machines stops last longer as they should: waiting for maintenance staff to come, discover the cause of the trouble, fix it, waiting for the operators to come back and resume production.

It can go the other way when production is strongly incentivized on units produced: any stoppage or breakdown jeopardizes the bonus and is immediately resented when maintenance doesn’t fix the problem fast enough.

What TPM is trying to do: give operators a sense of ownership of their equipment in order for them to take care, use it well, help maintenance technicians to find the causes of breakdowns by summarizing what happened before, and so on.

In order to achieve this, training must be delivered to both production and maintenance staff, focusing on the required cooperation for the sake of overall performance improvement. It will be a win-win cooperation: operators enriching their jobs with technical aspects and maintenance technicians being freed of low-qualification tasks for a better use of their real technical expertise. However, this must be done step by step.

Increasing operators’ knowledge of their equipment

Operator will use their equipment and machines correctly if they are trained not only for the use, but also a bit further into technical details. When operators have a basic understanding of how a machine works, they may be able to discover some causes of malfunction by themselves and give precious indication to maintenance team. With this focus, downtime can be reduced as maintenance does not have to go through a full investigation. If operators show interest and abilities, they may be trained further, to a point they can help maintenance with repairs, preventive maintenance tasks, adjustments, etc.

In my years as production manager with Yamaha, we brought teams of ladies to take care of the maintenance of automatic electronic components insertion machines. These ladies started as operators without any technical background, only feeding the machines. Step by step we trained them to take care of simple cleaning tasks, then adjustments, later exchanging more and more complicated mechanisms and finally be involved in major repairs.

Ensuring equipment is well-cleaned and lubricated

Before dreaming of repairing complex equipment, the journey starts with more mundane but important tasks: cleaning and lubrication.

But it’s more than that. Autonomous maintenance is about passing over  to operators the basic cleaning of the machines, lubricating and oiling, tightening of nuts and bolts, etc.

With these new tasks, operators will soon be able to take over daily inspection, diagnosis of potential problems and other actions that increase the productive life of machines or equipment. With appropriate prior training, of course.

Identification of emergent issues before they become serious failures

Cleaning and lubrication by operators is not a trick to reduce manpower costs by pushing tasks to lesser qualified people. On the contrary: TPM considers daily cleaning as an inspection and operators as subject matter experts. Indeed, operators using the machines and equipment daily are the best qualified detectors of early signs of problems. While cleaning they can detect: wear, unusual noises, vibrations, heat, smell, leakage, change of color, etc.

Using the machines frequently, they know best what is “as usual” and what is unusual. Someone hired only to clean and lubricate machines without using them would not be able to notice the forerunning signs of potential big trouble.

This daily inspection is key to reduce breakdowns by keeping the machine in good condition and by warning early – before breakdown – in order to remedy swiftly to unusual forerunning signs.

Freeing maintenance personnel for higher-level tasks

Putting skilled professionals in charge of challenges matching their expertise is certainly more attractive than asking them “to clean up other’s mess”, as maintenance staff frequently complain. Therefore the reluctance to train production operators for simple tasks and hand those over should not be a big deal for maintenance techs.

Production management should also see the opportunity to have better technical support for improvement and repairs, as skilled technicians are made more available. Of course, this comes at the expense of some daily minutes devoted to take care about machines instead of producing parts. In the long run, this should be a good deal, because less breakdowns, less scrap, fewer minor stops and faster changeovers thanks to technical improvements will pay back in productive capacity.

Finally, for production operators, the deal is to enrich their job with more technical content. For those immediately claiming acquisition of new skills deserve a pay raise, they should first consider that taking care of machines and equipment they are in charge is a basic expectation, not an extra requirement. Time will be given to do the daily maintenance routine. For operators it’s a shift of occupation content a few minutes a day.

Now this said, the question of a raise is to be considered in the context.

About the author, Chris HOHMANN

About the author, Chris HOHMANN

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