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Posts Tagged ‘bottlenecks’

In the first of this seven part series of thoughts on manufacturing constraints and scheduling, I started with a reference to Eli Goldratt’s The Goal.  Along the way, we placed links to various online sources for more information.  Obviously, the trove of information is deep.  You really should check them out.

In our own consulting practice, usually we find clients simply want some guidance.  And usually, we provide it ourselves.  Sometimes, we refer them to others with a reputation for solving problems. 

For Lean Manufacturing matters, we like to go to our guys Larry Lukasik and Jim Therrien.  They don’t have a website – they’re too busy doing good work helping companies go lean – but we happily connect them with clients with a need in this area. 

Other times, we’ve called in our good friend Dr. Donn Novotny (referred to earlier as the role model for Alex Rogo in The Goal).  Donn’s President of The Goal Institute, and is a master at solving complex problems with logical thinking processes.  He’s a master teacher of TOC and has worked with companies large and small, all over the world.

Frequently, we apply our Business Process Analysis, a modestly priced fixed-fee engagement whereby we help clients identify and resolve their own bottlenecks, constraints and process gaps.  Once identified, we’ve had great success in solving problems with better processes and, of course, software and technology. 

That technology slant seems to be the field-leveler these days.  The clients we have that are really committed to their tech investments gain real strategic competitive advantage – and most importantly, growth, compared to those with fear of the terrain, or whose commitment to real improvement often just doesn’t compare.

The global landscape is changing, faster than most recognize.  The Internet and its associated Widgets of Productivity are changing the landscape for all of us.  You embrace it, exploit it for your own purposes, or get run over by it.

In this series on Drum-Buffer-Rope thinking, I hope we’ve provided a little food for thought.  This stuff has been around for awhile.  And we’ve barely scratched the surface.  Most of it is about logical thinking processes — but necessary ones, at least in today’s manufacturing environment. 

At the least, I hope we’ve induced you to think about your own constraints, and what you can do about them. 

In solving these kinds of problems on a daily basis, our team gets to see a lot of the best (and sometimes, the worst) practices in action.  The common thread among the best of them is smart people leading smart teams operating under the assumption that, really, what other choice do we have?  Adapt and survive.  Else, not.

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To properly manage an acceptable production flow, relative to the demands of your customer (and in pursuit of on-time, on-budget), a set of rules derived from our previous post’s key points can be established, based on Theory of Constraints logic.  These points are intended to drive the logic of your MRP.  You can find them here, and they are summarized below:

1. Establish the due date requirements for the orders or demand. This provides the first and “ideal” drum to work to.

 2. Identify the CCRs (Capacity Constraint Resources) in the system.

 3. Develop a Drum or a schedule for the CCRs which makes best use of them and is in-line with the needs of the market. The drum is effectively the master production schedule which establishes the “drum beat” and control for the entire system.

 4. Protect the throughput of the factory from statistical fluctuations through the use of time buffers at critical locations. Time buffers are strategically located to protect the throughput of the entire system and to protect the due dates promised to customers.

 5. Use logistical ropes tied to the CCR drum schedules for each resource. The ropes synchronize all non CCRs to generate the timely release of the right materials into the system at the right time. Ropes ensure that operations upstream of CCRs are time phased to CCR requirements and operations downstream do not subsequently impede product flow.

In your overall scheduling, you should insert buffers along the way to protect the constraints from disruptions, expected or otherwise.  Your schedule (The Rope) releases material into your system on a timely basis, tied to the size of these buffers, in order to increase the likelihood of smooth, continuous flow.

A schedule, which above all is intended to be do-able and realistic, is only as good as the team’s ability to manage it, to make it happen.  Focusing on constraints and the correct imposition of buffers (in the right place and size) will help improve the success of your schedule, especially when Murphy raises his head, as invariably happens.  And to come full circle, by managing the critical resource constraints with good scheduling and appropriate systemic buffers, new smaller constraints may begin to appear.  At that point, you go back to step one, identifying and attacking the new constraints – in the ultimate process of continuous improvement.

Next up, some final thoughts…

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We looked earlier at identifying constraints, noting that it is fairly intuitive to observe that the constraint by definition is the weak link in the system.  How do we improve the throughput at the constraint without unduly increasing the expense or cost of doing so?

The answer lies in finite scheduling – The Rope, of Drum-Buffer-Rope thinking – in order to balance the flow (throughput) of our system.  The idea is to control the flow of production through the plant in order to meet sales (market) demand, with the least amount of manufacturing lead time, expense and inventory costs.

Modern MRP software can handle this for you.  In our experience over the years, we’ve seen many manufacturers with a high level of urgency regarding Scheduling.  Often, it’s the first thing they look at when evaluating an MRP system, but the last thing they implement – and for good reason.  Truly tight scheduling simply asks so much of the operators and managers.  We often guide clients to a rough-cut or gross requirements scheduling solution: it’s more important to get the order right, than schedule the shop down to the gnat’s behind – especially since attempts to do so are rarely successful.

All that being said however, The Theory of Constraints (TOC) school of thought, first introduced in Goldratt’s The Goal focuses on five steps to implementing an effective scheduled throughput, which I quote verbatim below.  For a fine (if lengthy) overview go here.

1. Identify the system’s constraint(s).
2. Decide how to exploit the system’s constraint(s).
3. Subordinate everything else to the above decisions.
4. Elevate the system’s constraint(s).
5. If, in the previous steps, a constraint has been broken, return to step one, but do not allow inertia to cause a system’s constraint.

The Rope provides for proper release to the manufacturing flow process, or as put by others, it aids in subordinating all else to the system’s constraint(s).  It’s your schedule.

Broadly speaking, you start by identifying all constraints within the system.  Make these the focus of your attention.  From these, derive your planning, scheduling and control of resources.  Once you’ve identified your capacity constraint resources (your CCRs, or your bottlenecks), schedule orders through them according to the capacity of the resource and market demand (your due date requirements). 

This schedule (or Rope), in effect is a looping-back to the Drumbeat of your demand. 

Next article, a little more info on Scheduling…

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Picking up from our prior post on Throughput…

To get an ideal flow time for a complex assembly, use the version of Little’s Law that stipulates (stay with me here): Flow Time = Inventory / Throughput

To estimate a flow time for an assembly process then, we measure the Inventory (in dollars) across the line or process, measure Throughput in terms of COGS (Cost of Goods Sold) and find their ratio.  This will provide a usable measure of Flow Time.

Same goes for WIP.  Little’s Law implies: Flow Time = WIP / Throughput.  If you reduce WIP, you may reduce your cycle time, but that’s a slippery slope.  If you reduce WIP without making other changes to the variables in the system, you’ll reduce your throughput, eventually affecting lead times and ability to deliver.  You can’t just reduce WIP to get lean.  You need what’s called a variability reduction to maintain or improve throughput with less WIP.

The ideal production scenario, then, is one that can be scheduled with a clear eye on the rhythm of product (the Drum) while cognizant of the cost effects of the Throughput component – setting up the ideal length of time, based on the right amount of inventory — for parts to reach the constrained area (the Buffer)

Next up, the Rope…

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The simple way, in theory, to resolve a constraint is to throw inventory ahead of it, right?  After all, with enough material coming into the bottleneck, you never have to worry about it being in a wait state, with its consequent waste.  That inventory is directly related to lead time, and thus, the ability to deliver to customers on time (i.e., “make the schedule”).  Of course, we know intuitively that there is a cost associated with this too, potentially greater than the cost associated with waiting at the bottleneck.

Which brings us to Little’s Law.  It’s named for John Little, who in the 1960’s, working out of a university in Cleveland, Ohio (my home town) defined something called queueing theory.  It states that the Length of a queue is equal to the Average Arrival Rate times the Average Waiting Time.  It’s pretty simple when you think about it.  It relates three performance measures in a production system, and states them as a basic manufacturing principle.  Little expressed this as:

L= λ W

(Thus, Length of the queue equals lambda (a mathematical expression here used for, basically, Throughput) times Wait)

You can Google this stuff ad nauseum, but suffice it to say it’s useful in identifying a lot of different process flows and bottlenecks, including your own.  It’s used in business to calculate wait times, planned inventory times, desired warehouse inventory turns, ways to reduce WIP (Work in Process), process flows, etc.

In production it can be simply applied: A machine that can process a part a minute (i.e., 60 per hour) and that has 500 pieces in front of it has about 8 hours of WIP.  (All examples assume a ‘static’ environment, i.e., all other things being equal and unvaried.)  By itself, this figure holds less value than it does in context: if throughput is indeed 60 pieces per hour, then 500 pieces can be pushed through in a day.  But if the process in question can utilize just 6 pieces per hour, then you have an 80 hour (i.e., two week) WIP backlog, and that’s probably not good.  Too much inventory makes for good on-time delivery perhaps, but costs a fortune in excess inventory.

But from this calculation, a trained observer can roughly determine whether the flow into a work center is too much (thus wasting too much inventory), or is a good fit.  The 500 parts seem ‘reasonable’ with a 60 piece per hour flow rate, but much less so with a 6 piece rate.  Plant managers can begin using that knowledge to determine rough cut estimates of just how much inventory needs to flow into a bottleneck to keep it from becoming a major constraint of the system.

Simple.  Intuitive.  But a useful tool for getting at core calculations about your own throughput, bottleneck identification, and what to do about them.  This is the Buffer component of Drum-Buffer-Rope.  Find your biggest constraint, and calculate the rough level of inventory (or Buffer) – no more or less – that is required ahead of it to keep that constraint from going into serious waste mode.

In our next article, we’ll look at flow time in assembly, and the effects of WIP.

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What’s a bottleneck?  Every production system consists in one way or another of a series of discrete steps, right?  We noted earlier that the rhythm of the production process is The Drum in DBR.  Exceed the capacity of one step in the process, and you have a bottleneck.  That’s not bad, it’s just natural.  But that bottleneck thereby defines the capacity of the system as a whole.

Each step has an input and an output component.  Looked at that way, it’s usually pretty easy to spot the bottleneck.  Is one step out-producing the ability of another step to absorb its output?  There’s your bottleneck.  Moreover, overall production only increases if you can increase the capacity of your bottleneck.  Until then, all other processes are subservient to the bottleneck.  It’s the old weak link in the chain concept.

A guy named Pete Abilla has summed up nicely the leading thoughts on managing bottlenecks here.

His thoughts come from multiple sources, but summed up, he points out five principles, which I quote directly (because someone has already invented this wheel, and as they say, a hack borrows, an artist steals…), as a perfect synopsis of how to treat your bottleneck.  Knowledgable readers will recognize these as basically The Five Focusing Points of TOC theory…

  1. Bottlenecks should never be idle; to lose time on a bottleneck, is to lose throughput.
  2. Never let a bottleneck run out of work. It’s okay to build inventory in front of a bottleneck.
  3. Increase productivity rates (offline and online processes) by reducing down-time, change-over time, and off-task time.
  4. Reduce defects by having Quality Assurance and Quality Control in front of a bottleneck, not after.
  5. Focus all improvements on the bottleneck.

The key point: identify your constraint(s), then manage it (them).  Every bottleneck solved creates a new bottleneck somewhere else.  Hence, the notion of continuous improvement.  But let’s not get ahead of ourselves. 

Instead, having now looked at the Drum, let’s look next at the Buffer…

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As alluded to in my prior post: With this entry, I’ll start a series of seven articles covering topics of interest to manufacturers.  In particular, we’ll delve into constraints… Eli Goldratt’s The Goal… the use of Drum-Buffer-Rope and Theory of Constraints thinking to solve those constraints… and scheduling.   Along the way we’ll point out a few worthwhile web resources, and mostly, hopefully, get you to thinking… about your constraints.

At the heart of the line of thinking that has been named “drum-buffer-rope” is the basic need to hurdle the obstacle –or relieve the ‘constraint’ — that is found in any process.  It’s often about scheduling, as alluded to in our prior post.  It is a basic construct of Eli Goldratt’s “Theory of Constraints” as first outlined in his book The Goal (where it first appears in the context of a weekend outing of scouts on a hike, and the overall effect on the troop of the slowest boy’s struggle to keep up).  It’s beyond the scope of this blog to detail all the many elements of the The Goal; however, for a great synopsis, you can go here.  Typically applied to production problems, the basic construct is as described below.

It’s called Drum-Buffer-Rope as a metaphor for each component.  The Drum sets “the beat” or the rhythm by which production occurs, or should occur.  In fact, the Drum is the constraint or bottleneck inherent in a production system.  Nothing can exceed its beat, naturally, and hence running at full speed, it becomes the constraint.

The Buffer defines a solution to the situation around or outside the constraint, where a process upstream cannot produce as much as the bottleneck or constraint requires.  So, we create Buffer, or excess, to ensure the constraint is not… constrained.  It’s meant to ensure that the Constraint (or Drum) never has to wait, since waiting is pure waste.  Buffer is inventory.  Having it in excess leads, theoretically at least, to reduced lead time, and thus, better throughput.  But of course, excess inventory is costly, and thus wasteful in itself.

Hence, the Rope.  The rope serves to signal a non-bottleneck process upstream when to speed up or slow down, in an effort to maintain a constant but acceptable flow, to and through any constraint.  It’s the throttle mechanism on a perfect world of production.  In Lean, this is sometimes called “Pull” or Pull Scheduling.  In software, we can use data about demand to know when to push or pull the schedule.

Taken together, it’s a construct built on common sense to solve a complex problem that at one time or another affects production almost everywhere in manufacturing.

How do we maximize throughput, smooth out the bumps, reduce costs and eliminate waste… all at the same time?  In other words, how do we achieve maximum capacity at the bottlenecks with minimal waste, defects and expense?

We’ll head there next post…

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