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.

Proceed to part 5 and conclusion of this series: How to identify the constraint of a system? Part 5

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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