A Motor For Production Drum-buffer-rope is the Theory of Constraints production application. It is named after the 3 essential elements of the solution; the drum or constraint or weakest link, the buffer or material release duration, and the rope or release timing. The aim of the solution is to protect the weakest link in the system, and therefore the system as a whole, against process dependency and variation and thus maximize the systems’ overall effectiveness. The outcome is a robust and dependable process that will allow us to produce more, with less inventory, less rework/defects, and better on-time delivery – always. Drum-buffer-rope however is really just one part of a two part act. We need both parts to make a really good show. If drum-buffer-rope is the motor for production, then buffer management is the monitor. Buffer management is the second part of this two part act. We use buffer management to guide the way in which we tune the motor for peak performance. In the older notion of planning and control, the first part; drum-buffer-rope, is the planning stage of the approach – essentially the overall agreement on how we operate the system. The second part, buffer management, is the control system that allows us to keep a running check on the system effectiveness. However, I want to reserve the word “planning” and the word “control” for quite specific and established functions within the solution, functions that we will investigate further on this page. I want to propose that we step out a level and instead use the terms “configuration” and “monitoring.” Using this terminology the configuration is drum-buffer-rope and the monitoring is buffer management. Let’s draw this; The way that we configure the solution, the way that we configure the; drum, the buffer, and the rope, will determine the characteristics and the behavior of the system as a whole. Buffer management allows us to monitor that behavior. The use of the terms configuration and monitoring will allow a more critical distinction to be developed once we introduce the concepts of planning and control. This, I hope, will also clarify some of the confusion that may exist over the dual role of buffer management. Keep this model in mind as we will return to it. Now, however, we must return to our plan of attack and work through the development of the solution. Interested? Then let’s go.

Our Plan Of Attack On the measurements page we introduced the concept of our “rules of engagement” which is to define; the system, the goal, the necessary conditions, the fundamental measurements, and the role of the constraints. Then on the process of change page we introduced the concept of our “plan of attack” – the 5 focusing steps that allow us to define the role of the constraints. Let’s remind ourselves once again of the 5 focusing steps for determining the process of change; (1) Identify the system’s constraints. (2) Decide how to Exploit the system’s constraints. (3) Subordinate everything else to the above decisions. (4) Elevate the system’s constraints. (5) If in the previous steps a constraint has been broken Go back to step 1, but do not allow inertia to cause a system constraint. In other words; Don’t Stop . Let’s also return to our simple system model which we have so far used in much more general terms and apply it to drum-buffer-rope. As you will recall it has 4 sections, or departments or whatever you would like to call them; a beginning, a middle, a near-the-end, and an end. Our system has to interact with the outside world, so let’s draw in an input and an output. Raw material flows in and finished product flows out. In a for-profit situation sales flow in and expenses flow out, the difference – profit, is captured by the system. We showed these flows previously in the section on measurements. Now we are ready for the next stage, the first step in the 5 step focusing method – identify the constraint.

Identify The System’s Constraints In fact we know where the constraint is in our simple system presented here based upon the discussion in the earlier section on measurements. It’s located near the end of the process. This isn’t at all an unusual place to find a constraint. Think about it for a moment. If the constraint was located near the beginning, then all the downstream steps would always be waiting for work. In that situation management would most probably go about purchasing further capacity until they move the constraint further down the process and then bury it in work-in-process so that it is no longer visible. Let’s draw the constraint in. As we know from the previous section on production, the constraint, the slowest step, beats out the rate at which the whole process can work at. Therefore it becomes the “drum” of drum-buffer-rope. Of course we forgot something – work-in-process. If our model system is to be anything like our own reality, then it is probably full to the gills of work-in-process. We had better add this to our model as well. Work-in-process of course serves a useful purpose in such a system; it decouples each stage from the stages before and after. If you don’t know what to protect, then you might as well protect everything. However chances are that, even with all that protection, the work that was required at the time wasn’t the work that was waiting in the pile of work-in-process. And of course it means that the time required for any job to traverse the system is much longer than necessary. In any case we don’t need all of that work-in-process anymore if we are going to use drum-buffer-rope. So we have completed Step 1 – identify the constraint. The next step, step two, is to decide how to exploit the constraint.

Exploit The System’s Constraints To make sure that the constraint works as well as possible on the task of producing or creating throughput for the system we must ensure that we exploit it fully – essentially we are leveraging the system against the full capacity of the constraint. This means not only making sure that it is fully utilized, but also making sure that the utilization is fully profitable. If you remember back to the P & Q problem or the airline analogy, is quite possible to have everything utilized but not make as much profit as is possible. If we increase the output of the constraint, then the output of the system as a whole will increase also. One of the most effective tactics for exploiting the constraint, once identified, and improving its output is to write a detailed schedule for that particular resource and that particular resource alone – and then to adhere to that schedule. This is the “plan” in this context. Our day-to-day planning “falls out” as a consequence of the decisions that we make while configuring the implementation. Let’s add this to our model. We now have a local plan for just one point, the most important point, the drum. If, at the same time, we hold the input constant then the additional output from continued exploitation must come from work-in-process already in the system. As a consequence work-in-process and hence lead times must go down. In effect we begin to drain the system. Let’s show that. Let’s be clear however, work-in-process does not have to decrease under drum-buffer-rope, but usually there are sound reasons for doing so – reduced lead times, increased quality, and increased throughput. We will investigate all of these sometime later under the heading of the role of inventory. The primary objective of Theory of Constraints however is always to move the system towards the goal, and usually that means increasing throughput first. Inventory reduction is secondary and often a consequence of increasing throughput. If we continue to operate in this fashion we can reduce work-in-process considerably. Let’s show this before introducing some further drum-buffer-rope concepts. In fact we have completed the second step; we have decided how to exploit the constraint. We used a simple example of writing a schedule, there are many more ways to exploit a constraint and some of these are mentioned in the next page on implementation details. However, we need to move on to the third step, subordination of the non-constraint resources.

Subordination – Protect The System’s Constraints Sometimes using the word “protect” makes it easier to understand this step than using the correct term which is “subordinate.” In fact, we subordinate the non-constraint resources in order to protect the constraint and the system as a whole. Let’s examine this is a little more detail. In the process of change page we described subordination as avoiding deviation from our plan, and the plan in this case is our constraint exploitation schedule in the previous step. We described deviation from plan as (2); (1) Not doing what is supposed to be done. (2) Doing what is not supposed to be done. We can therefore describe subordination as; (1) Doing what is supposed to be done. (2) Not doing what is not supposed to be done. By doing what is supposed to be done in accordance with our plan we protect the constraint and the systems as a whole. Moreover, by not doing what is not supposed to be done in accordance with our plan we also protect the constraint and the system as a whole. Let’s examine this with our simple model. As we use up our supply of excess work-in-process, it is likely that the constraints will begin to “starve” from time to time. Work will not arrive in sufficient time for it to enter the constraint on schedule. We need to replace our local safety everywhere (our excess work-in-process) with some global safety right where it is needed, in front of the constraint. We need to buffer the constraint. We need to do what is supposed to be done in order to protect the constraint from shortages. In fact we would normally have made our buffering decisions before we even began and therefore reduced our work-in-process and lead time in line with these pre-determined targets.

An Initial Buffer Sizing Rule Let’s assume for a moment then that the lead time allowed for work to travel from the start of the process to the start of the constraint was 18 days prior to the implementation. Well, in fact, it could be 18 hours for electronics or the paper work in an insurance claim, or it might be 18 weeks for heavy engineering. But let’s use days in this example. The rule of thumb to apply is to halve the existing lead time (3). Therefore the new lead time becomes 9 days. If halving the lead time sounds horrendously short, it is not. Most of the time the current work-in-process is sitting in queues doing nothing. You can easily check this for yourself – got out and tag some work with a flag or a balloon or a bright color and then watch it. It will sit. This 9 day period becomes our buffer length. To this 9 day buffer we apply a second rule of thumb and divide the buffer into zones of one third each (4). We expect most work to be completed in the first 2 thirds and be waiting in front of the constraint for the last third of the buffer time. Thus we expect our work to take about 6 days of processing (and waiting-in-process) and 3 days of sitting in front of the drum. If 3 days sitting in front of the constraint sounds terrible, then remember that prior to the implementation, the system allowed work to sit for at least another 9 days. Nine plus 3 is 12 days sitting. Which would you rather have 12 days or 3 days? More importantly, which would your customer prefer? We now can protect our system constraint by ensuring that there is always work for it to do. Thus we ensure its effective exploitation – and with much less total material or lead time than before. Let’s add the buffer to our diagram. Let’s make sure we are clear about the definition of the buffer. “For all practical purposes the TIME BUFFER is the time interval by which we predate the release of work, relative to the date at which the corresponding constraint’s consumption is scheduled (5).” Please be careful, on the diagram above we have drawn units of time – the zones and the buffer – as space on our diagram. Don’t let this confuse you. The zones equate to time allocated in the plant to protecting an operation whose position and function is critical to the timeliness and output of the whole process. The zones do not equate to the position of work in the plant. In fact we will return to this shortly and try and draw the diagram more realistically to represent time. Why is this whole period from material release to the constraint considered as the buffer? Schragenheim and Dettmer consider that this is one of two unique aspects of buffering in Theory of Constraints. “The reason buffers are defined as the whole lead time and not just the safety portion is that in most manufacturing environments there is a huge difference between the sum of the net processing times and the total lead time. When we review the net processing time of most products, we find it takes between several minutes and an hour per unit. But the lead time may be several weeks, and even in the best environments several days. Consequently, each unit of product waits for attention somewhere on the shop floor for a much longer time than it actually takes to work on it.” “So it makes sense not to isolate the net processing time, but to treat the whole lead time as a buffer – the time the shop floor needs to handle all the orders it must process (6).” The other unique point is that buffers are, as we have mentioned, measured in time. Firms in non-drum-buffer-rope settings consider a buffer to be a measure of physical stock; 6 jobs, or 6 orders, or 10 batches, or 4000 pieces, or whatever. In drum-buffer-rope a constraint buffer is a measure of time; hours or days of work at the constraint rate located between the gating operation (material release) and the constraint. In fact, there are two ways to look at a buffer, either from the perspective of a single job, or from the perspective of the system as a whole. Let’s consider this for a moment. Let’s assume for the sake of simplicity that all of our jobs are of equal length. Let’s assume then that each one takes 1 day of constraint time. In this case each job has a 9 day buffer to the constraint. That is, it is released 9 days prior to its scheduled date on the constraint. This is the perspective of a single job. The constraint, looking back, will see 9 one-day jobs at various stages in the process; this is the perspective of the system as a whole. What then, all else being equal, if all of our jobs now take half a day on the constraint? Each job sees a 9 day buffer, the constraint looking back will see 18 half-day jobs at various stages in the process, but the aggregate load is still 9 days, this is the perspective of the system as a whole. Let’s do this one more time. Each job now takes quarter of a day on the constraint. Each job still sees a 9 day buffer, the constraint looking back will see 36 quarter-day jobs at various stages in the process, but the aggregate load is still 9 days from the perspective of the system as a whole. It is time that is the measure of the buffer.

Why Is The Constraint Buffer Size & Activity Determined By Time? Let’s labor this point for a moment because it is so important. Measuring a constraint buffer in units of time is unique to drum-buffer-rope because acknowledgement of the existence of a singular constraint within a process is unique to drum-buffer-rope. We can apply this to both the constraint buffer size and the constraint buffer activity. Let’s look at constraint buffer activity first. By considering only one station, or step, or procedure, we need only to know one set of average times for that place or action for all of the different types of material units that pass through it. We could look at this as follows; At a manufacturing constraint an hour is an hour but the number of units may differ The number of physical units may differ because different types of material using the same constraint may use different amounts of constraint time. In fact, even the same type of material will display some variability unless the constraint is a totally automated procedure – but these will largely average out. How about constraint buffer size then? The unique perspective brought about by the designation of a singular constraint allows us to define the length of the buffer in time also. Essentially the buffer is sized and “sees” the duration from the gating operation to the constraint due date. Moreover the buffer “sees” committed demand – work that has already been released to the system. Constraint buffers, divergent/convergent control point buffers, assembly buffers, and shipping buffers are all of the same basic nature. Maybe it is much simpler to say that; We protect time (due date) with a time buffer There is, however, one other buffer type that we are likely to come across in manufacturing – a stock buffer. There are two places that these occur at in manufacturing; they are at raw material/inwards goods in all process environments and at finished goods in a make-to-stock environment. These are actually supply chain buffers; they represent the two places that the supply chain must interact with processing – before the beginning of the process and after the completion of the process. We need to ensure that we always have an adequate supply of raw material prior to the process to meet consumption and we need to ensure that we always have an adequate supply of finished goods post-production to meet demand. We will examine these types of buffers later on this page. They are also examined in more detail on the supply chain pages – especially the replenishment page. However, let’s confine ourselves at the moment to constraint buffers. We need to labor the issue that the constraint buffer is a measure of time. Let’s do that.

A Journey Through Time Many, many, people say that they do understand the definition of a drum buffer or of a constraint buffer when the evidence is that they do not. Too often our prior experience causes us to think of buffers in terms of physical stock, and too often we consider zone 1 as “the buffer.” Let’s see. The buffer is the whole of the duration of the part of the system that the buffer protects. Did I overstress the point? I don’t think so. Check here for more discussion on continual mis-understanding of buffers in drum-bufffer-rope. In part, this is due to our prior manufacturing experience with MRP II systems and push production which tends to blind-side our interpretation (see the sections on Buffer The Constraint and Local Safety Argument in the next page – Implementation Details – for further development of this aspect.) In part, the problem also lies in the way we try to draw time as space on our simple diagrammatic representations. The only way to draw time is to draw a sequence of diagrams. Let’s do that. We will follow a slice of work – ones day’s worth – through the process to the drum. We will use our 9 day buffer as we derived above, so this slice of work is the drum’s work for one day 10 days out from the scheduled processing date. There are 5 products (units, jobs, batches, whatever) in our slice. The products are “lilac,” “red,” “green,” blue,” and “orange.” The time interval, for the sake of clarity in this example, is course – days – rather than finer divisions of hours or less that we might expect to find in reality. Imagine that within the departments (“beginning” and “middle”) of our generic process we have the tools of our particular trade; be they desks in a paper trail, admission or beds or clinical units in a hospital, or work centers in a manufacturing system. The 5 products could be at any time waiting or moving between jobs or being worked upon. The resolution of this detail isn’t important to us here. Probably in the day before the release date the planner knows what will be released. The planner might even have the orders “cut” and waiting but unreleased (and hopefully unknown to the floor – to avoid people working ahead of time). Let’s draw this. The orders may exist on a plan but they are not yet released. We draw the units outside of the system even if they currently have no physical presence other than paper work or an entry on a scheduling system. We have also drawn a timeline in. It is colored according the buffer zones. Zone 3 is the “green zone,” zone 2 is the “orange or yellow zone,” and zone 1 is the “red zone.” On the first day of the schedule all the products are released (as scheduled) and are in zone 3 of our time buffer. Their physical location at the end of the day is as follows. Lilac might be small batch or a simple process that is completed quickly, it moves forward further (and maybe faster) than the rest. After another day we are at day 2 and still within buffer zone 3 the process looks like this. We can see that red has moved quite quickly relative to the others and blue hasn’t moved at all. How does this happen? Different jobs travel through different routings, and have different wait times (because of other jobs in front of them) and different processing times (either because of different batch size or different work). And of course sometimes things don’t always go as planned; we have break-downs, people are absent, and “stuff happens.” By day 4, one day into buffer zone 2 we see the work has evolved as follows. Blue still hasn’t moved, however, the others are progressing well. The next day, day 5 (buffer zone 2), the work looks like this. The green and red jobs are complete, the lilac and orange jobs are progressing well and blue is moving forward at last. At the end of day 6 – the last day of buffer zone 2 we see the following. Three of the five jobs are completed by the end of buffer zone 2, and two are lagging behind. Because the end of day 6 is the starting of day 7 and the first day of zone 1 (the red zone) we have a buffer penetration. Two jobs that ought to be finished by now have not been finished. They must be located and appraised to ensure that they will reach the constraint in the remaining time. Let’s go out to the end of day 7 and see what happens. Now 4 jobs have been completed. The lilac job was completed sometime on day 7. The blue job will have been located and checked to see that it will meet the schedule and be available at the drum by the end of day 9 – the last day of zone 1 – or preferably sooner. We would have preferred that most jobs were completed by the end of day 6, but sometimes “stuff happens” and not all jobs are complete at that time. The blue job might require some “assistance” to ensure its completion. Let’s now look at the situation at day 8. Phew! We find at the end of day 8 – with just one day to spare – that all of the jobs are completed and waiting to be processed on the constraint according to the schedule on day 10. Zone 1 – the red zone – of the buffer was penetrated by as much as 2 days by the blue job and as much as 1 day by the lilac job. However the drum is now fully protected and the drum schedule will not be compromised, our exploitation strategies are fully protected by adequate subordination of the other resources. So, let’s reiterate once more; buffer zones equate to the time allocated in the plant to protecting operations whose position and function are critical to the timeliness and output of the whole process, buffer zones do not represent the physical location of work in the plant. We know how to protect the constraint, now let’s see how to protect everything else.

Subordination – Protect Everything Else So, we know now how to protect the constraint using a buffer, we therefore know how to do the first part of subordination – doing what we are supposed to do in order to protect the constraint. Now we need to examine the second part of subordination – not doing what we are not supposed to do. First, let’s repeat the diagram that we first drew prior to our journey along the buffer. The drum, gating operation, and shipping are all stable. That is they are now all operating at the same rate, the drum beat. If we were successful in exploiting the constraint and increasing the constraint rate and output, and demand increased as a consequence of the reduced lead time, then at this stage the input rate must be also be increased to match the drum beat so that work-in-process and the buffer remains stable. In order to maintain stability in this system, the rate at which the gating operation allows the admission of new work to the system must be the same as the rate of consumption at the drum. What would happen then if our constraint breaks down for a short period? It has no spare capacity, so we can’t speed it up (allow it to work longer) to catch up to the work again. If we were to continue to admit work as though nothing had happened then work-in-process would increase a little. Probably not enough to notice, but over a couple of different instances it would begin to build up. Thus we need to make sure that we admit new work into the system at the same rate as the constraint is consuming it. The constraint as we know is the “drum” of our system, beating out the rate at which the whole system works, including the gating operation. The rate is communicated to the gating, or first, operation by the “rope.” We need to add this to our diagram. If you like, the schedule of the gating operation is the schedule of the drum off-set by a rope length of time. The rope length is the same as the buffer duration ; the gating rate is the same as the drum rate. “Tying” the rope between the drum and the gating operation ensures that excess work can not be admitted, or that normal work can not be admitted too soon. This is part of not doing what is not supposed to be done in order to protect the system from excess work-in-process. Excess work in process results in longer than necessary lead times and poorer quality. Ultimately excess work-in-process also impacts upon the throughput of the constraint. Another way of looking at the rope is to consider it as a real time feedback loop between the drum and the gating operation. Although the constraint can not recover from down-time, hence the need to exploit and protect it, the non-constraints can recover from down-time. Generally the non-constraint parts of our system don’t work at the same rate as the whole system – at least over short periods of time. The non-constraints have sprint capacity. They can and do process more work when necessary to catch-up after a “bump” in the system by operating at normal rates for longer periods. They can also process less work when not needed by operating at normal rates for shorter periods. We might consider the increased duration of non-constraint processing (in order to catch up) as the “doing what is necessary” part of subordination, and the reduced duration of non-constraint processing (to avoid over-production) as the “not doing what is not necessary” part of subordination. It is critically important that we do this. In the Toyota production system, kanban perform both of these functions. In drum-buffer-rope this is performed by the “roadrunner” concept. When we have work to do, we do it. When we don’t have work to do, we don’t do it. We saw this in the form of the traffic light analogy earlier in the page for process of change. Non-constraints should never “slow down,” they should either be fully-on or fully-off (maybe that should be normally-on or normally-off). Either creating throughput or protecting throughput. We will revisit this theme on the next page; implementation details. We have seen how we now have 9 days of work in process in our example – down from the initial 18 days. There are 6 days of work in process in zones 3 and 2 and three days in zone 1. But we can think about it in another way. By halving the work-in-process we have removed 3/6ths of the work from the system. We have another 1/6th sitting in front of the constraint, so in effect we have just 2/6ths or 1/3rd of our previous work-in-process actually on the floor being actively worked on or sitting in queues. Imagine your process at it stands today but with just 1/3rd of the work actively being worked on by the non-constraints or waiting-in-process; wouldn’t things really begin to flow in that situation? Nevertheless, in order for that material to flow, it is critically important to protect sprint capacity by proper subordination. Sprint capacity interacts with overall buffer size and hence manufacturing lead time. We will investigate sprint capacity more in the next section. Protecting sprint capacity means that we never admit work into the process just to keep people busy – never. Of course you will have noticed that we have so far used the departmentalized version of our system model. To some extent this was intentional because when you first approach a drum-buffer-rope implementation, the process will be departmentalized. However, what we would like to see after a short while is a better appreciation of the system as a whole. Conceptually it should look more like this; Until now we have ignored that part of the process after the constraint – usually from the constraint to shipping in make-to-order, or to the warehouse in make-to-stock. This part of the process is tied to the shipping date by a second rope most often referred to as the “shipping rope.” It is via this rope that the drum is tied to the market demand. So now we can be sure that just enough new material is allowed to enter the system to protect and satisfy the drum consumption, which in turn supplies the market demand. We don’t admit work for which there is no demand. Therefore, we have indeed subordinated everything else to the constraint. For the shipping buffer, we again apply the same rules of thumb as we used for the constraint buffer. Let’s assume, for the sake of the ease of the mathematics, that the process downstream from the constraint to shipping currently takes 6 days. We halve that to give us a new lead time of 3 days. And then we divide that into buffer zones of thirds and expect almost all work to be completed after 2 days and either waiting for shipment or already shipped 1 day prior to the shipping date. This is what we arrive at; Now our shipping date, or delivery date, is protected as equally well as the constraint is. We should therefore expect very good on-time delivery or due date performance. This of course is especially important for make-to-order firms and is most often a definite competitive advantage. Thus our original 24 day process becomes a quoted delivery time of 12 days. The system should be able to produce more because we will have made every effort to exploit the constraint, and the non-constraints only work on material that is required to support the constraint schedule. So, to summarize, subordination is the instruction to the non-constraints. It has two main components. Firstly; in order to protect the system as a whole we must not starve the constraint – we must not underload the system. This will ensure maximal output as per the exploitation strategy of the constraint. Of course we could make the buffer quite large and never have to worry about starving the constraint, but that is where most systems are today (and they still starve the constraint). So we need to do something else as well. Secondly; in order to protect the system as a whole we must not flood the non-constraints – we must not overload the system. This will ensure adequate sprint capacity to ensure maximal output as per the exploitation strategy of the constraint and high due date performance. It will also ensure a much reduced lead time. Thus we have covered all three aspects; the drum, the buffer, and the rope. We have also covered the first three steps of the 5 focusing steps; identify, exploit, and subordinate. If we have fully exploited the leverage point, and subordinated everything else, then the next thing to do is to elevate the constraint. But first, let’s examine an alternative initial buffer sizing rule.

An Alternative Initial Buffer Sizing Rule So far we determined our initial buffer sizes by taking 50% of the existing lead time over the part of the system that we wished to protect. There is another lesser-known rule that we can also use. We can take 3 times the back-to-back time for a job to transverse that part of the system that we wish to protect. From time to time jobs are expedited for a number of reasons, therefore people will know from direct experience, or will have good intuition, for the back-to-back time and from that a buffer duration can be obtained.

Elevate The System’s Constraints Elevation may require that some additional investment be made to purchase additional capacity that will produce additional throughput, preferably at more than pro-rata. Remember we are trying to decouple throughput from operating expense thereby driving productivity and profitability up; We also know that elevation is most often the place where reductionist/local optima proponents start, however elevation is the place where systemic/global optimum proponents get to last. It costs you less doing it this way, of course you have to think – but then it’s just common sense – and you make more money or more output. If we don’t decouple throughput in for-profit organizations and output in not-for-profit organizations from operating expense and investment then we simply are not doing a very good job.

If A Constraint Has Been Broken, Go Back If at any time a constraint is broken then we must look for the next constraint. In fact we should know from buffer management where it will be before we even get there. However, many times an initial physical constraint is broken and a policy issue takes its place. Goldratt’s admonishment not to let inertia become a system constraint is a plea to look at which policies block us from moving forward even further. Really this is a plea to Don’t Stop!

Some Definitional Subtleties There are a few traps for those of us who are new players. Some of the definitions have changed over time. In this case to be forewarned is to be forearmed, let’s do that. Here we have used the term “drum” to describe the entity that determines the rate at which the whole system works – be that an internal constraint, or as we will see, an external market demand. However the term “drum” is also used to describe the drum schedule, in fact some insist that the drum is the schedule. Clearly when the constraint is in the market this definition makes more sense. Both are in use, some would argue that these different definitions are simply different manifestations of the same concept. If we accept this, then that ought to keep everyone happy. Likewise, the “rope” has been used here to describe the off-set between the drum and the gating process; however, it too is subject to a more restrictive definition of the “gating schedule.” Once again these are different manifestations of the same concept. They are not mission critical.

Buffer Management – Make-To-Order We have so far examined the development of the drum-buffer-rope solution – our motor for production – and we have done that within the framework of our 5 step focusing process. We also presented a model for the configuration and monitoring of this solution, to this we have added a local planning aspect; our schedule for the exploitation of the drum. Let’s repeat the model here and note that we are looking at the specific case of make-to-order. We now need to develop the monitoring part of his model; we need to address buffer management. We now know what buffers are and we know their purpose, however we still need to know better how to interpret and utilize the information that they can provide. And in order to do that I believe that we must subdivide their impact into two distinct functions. They are as follows; (1) Local Control ; the day-to-day exception reporting that indicates when there may be a potential due date violation. (2) Global Feedback ; longer term trend-reporting that suggests a particular buffer needs to be resized to be fully effective. Buffer management is crucial; it filters important signals from the day-to-day noise of the system thereby alerting us to potential problems before they become real problems and it provides a self-diagnosis that neither too much and nor too little protection is made available for each case. The self-diagnosis feeds back into our configuration and guides improvements in the overall dynamics of the implementation. Let’s modify our model to incorporate this. Thus we still have planning and control, but it is local and within the context of the overall design of the implementation. Schragenheim & Dettmer have an important definition of control, lets repeat it here (7); “A reactive mechanism that handles uncertainty by monitoring information that indicates a threatening situation and taking appropriate corrective action before the threat is realized.” The subdivision of buffer management into local control and global feedback will, I hope, make it easier to understand this important concept. Let’s now investigate the various stages of local control and then global feedback in a make-to-order environment.

Local Control – Buffer Status We release work a rope length ahead of the due date at the point that the buffer protects. In most cases that is more than sufficient to insure that the work arrives in good time. But as we know sometimes stuff happens. We need some local control to ensure that when stuff does happen we know the correct priority to return the system to one of stability. We need to know the status of the current make-to-order work orders that are already released to the system to be worked upon. Schragenheim defines the buffer status as follows (8); Buffer Status = (Buffer Duration – Remaining Duration) / Buffer Duration The buffer status is synonymous with buffer consumption. Let’s look at an example. Here we have the buffer status for 6 jobs due in the next 5 days. Jobs 1, 3, & 5 are completed as of today. For jobs 3 & 5 that means they were completed in good time, their buffer status is less than 66% at completion. Job 1, although now complete, must have been problematic a day or so ago as its buffer status had reached 78%, however it is now completed and no longer a problem. Of the current jobs; job 6 with a buffer status of 44% is no problem, we should leave it alone. However jobs 2 & 4 both have a buffer status of 66% or more. They have both begun to penetrate zone 1 of the buffer, the red zone. Of the two, which is the greater priority? Clearly it is job 2; this is the one with the greater buffer status value and is the one that we should first concentrate our attention on. Thus the buffer status drives the work order status once the job has been released to the system. We therefore have local control by exception in order to meet our global objectives. Buffer status is viewed from the perspective of a single job.

That’s Nice – But We Have Short Ropes & We Have Long Ropes What then, where we have short ropes and long ropes, surely that is more complicated? Again the buffer status allows the direct comparison of work orders that have differing buffer lengths within the same process. Let’s have a look. We have jobs in the system which have 3 different rope lengths – 9, 6, and 3 days. The shorter jobs generally go through far fewer processes than the longer jobs. Some short jobs, job 4 for instance, will be released later and finish earlier than longer jobs, such as jobs 5 or 6. Nevertheless, we know which jobs need attention; those that have used up the first two zones and penetrated zone 1 of the buffer, the red zone. Those in zone 2, the yellow or orange zone, may require a watching brief but we leave them alone least we begin to tamper with the process – and we wouldn’t want to do that. Don’t confuse short jobs and long jobs with “rush” and “standard” orders. That particular prioritization takes place in the order queue and not in the manufacturing lead time.

Local Control – Work Order Status In a make-to-order system where there is a commitment to meet a due date, then; Current Work Order Status = Buffer Status It may seem redundant to state this, however, it will become clearer when we deal with make-to-stock. In a make-to-stock environment the stock order status can vary over time due to the natural variability of the process itself and due to changing market demand as well. This additional factor is absent from the make-to-order environment, here we have a commitment and we must meet that commitment.

Local Control – New Work Order Release Priority Ever been in the situation where you have a nice workable schedule – all the critical areas have been satisfied (all the “squeaky wheels” are oiled), and then someone announces “you know the material that they said would be on the truck this morning, well its not!” What are you going to do? This job is down for the gating operation this morning, you don’t have it, and it’s the Friday before a long weekend. Sound improbable? Not at all. Officially you can wait. You can wait until up to 50% of the buffer to the next stage has been consumed. Until then the work can still be released, after that it must be rescheduled. In fact the same logic can be applied when back-scheduling from the shipping date. Sometimes commitment to different customers and/or varying rope lengths will result in a conflict where more than one job simultaneously requires the constrained resource. One solution is to start some work on the constraint even earlier than required. If a conflict still exists then some work may have to start later than we would like, in effect the late start pushes that job out so that it begins to consume part of its shipping buffer. Again, so long as not more than 50% of the shipping buffer is consumed then the job can proceed, otherwise the commitment to the client must be renegotiated – the due date must be pushed out further (9). Stepping back one step further, the same logic applies in-turn when back-scheduling from the drum date to material release, now for a different reason than material lateness; we can apply the same rules as before. In fact, this is really local planning rather than local control, but it is easier to understand now rather than earlier.

Local Control/Global Feedback – Work Order Zone 1 Buffer Penetration We want to insulate our customers from the variability and dependency within our system. We can do this in two ways; (a) proper subordination so that non-constraints have adequate sprint capacity, and (b) buffering so that even when sprint capacity is exhausted for what ever reason (stuff sometimes happens) we still have some safety time up our sleeves. Of course, we have “rolled-up” our local safety everywhere into a few discrete and critical places where it will be of maximal benefit. Work order status – our buffer status – tells us in real time when to facilitate an order, and in what priority we should do so if more than one work order requires facilitation at the same time. It is local, immediate, and pre-emptive. A buffer status that exceeds 66% has consumed more than 2/3rds of its buffer capacity, colloquially we say that it has penetrated the red zone. Until the order is complete and at the buffer origin we don’t know to what “depth” the red zone has been penetrated. However, once the order is complete and the degree of penetration is known we can call this degree of penetration a “buffer hole.” Let’s draw this. Buffer holes are our measure of system stability. They are global, aggregate, and historic. Trends in buffer hole behavior alert us to changes in system dynamics. Caspari and Caspari address this aspect particularly well (10). What then is an acceptable frequency for buffer hole occurrence? Well that depends. In a relative sense buffer holes should neither be too frequent nor should they be too rare (8). A more concrete suggestion is something less than 10% (11). Clearly the zone 1 buffer hole operates in only a limited number of cases. It is a fine example of exception reporting. After all we want a robust system, not one that must be pampered every hour of the day. If we take our example of a 9 day constraint buffer we would expect most jobs to be completed before 6 days and to be waiting at the constraint 3 days prior to their scheduled operating date. Of the remaining few we must go and look for them in the prior operations to ensure that they will reach the constraint before their scheduled operating date there. If we then look at aggregate data for, let’s say 25 jobs with roughly a 5% level of buffer hole occurrence, then that data might look something like this; We will find that most jobs arrived during zone 2 or the orange/yellow zone of the buffer, some will even arrive in zone 3, the green zone. In this case, however, 2 jobs penetrated zone 1, the red zone. It is the penetrations into the red zone that are our buffer holes. Buffer holes are viewed from the perspective of the system as a whole.

Local Control/Global Feedback – Work Order Performance Measures From the buffer hole data we can construct a more meaningful measure. In the measurements section we discussed two local performance measures; unit days late and unit days wait. In a for-profit environment these are known as throughput-dollar-days (late) and inventory-dollar-days (wait). I prefer to add “late” and “wait” to these measures because to those who are unfamiliar with the terms it is hard to know what they mean or their significance. And these two measures are so important to the overall success of drum-buffer-rope that we can’t afford to lose people through obscure terminology. The immediate impact of a hole in the buffer is to send the “buffer manager” in search of the offending job and ensure that it will indeed make the constraint by the appropriate time. In the first instance we are really interested in only one thing, that there is an incidence of a problem – a buffer hole – and whether the problem requires assistance or not. However, we need to quantify this better once it is rectified; was it short by a day or 2 days or by how much. And was it a valuable job to the firm or not. In other words was it a large or valuable job that was very late, or a small job that was just a fraction late to the zone 1 buffer. Multiplying the final lateness to zone 1 by the throughput gives us the correct answer – throughput-dollar-days late. It gives us a measure of the severity of the problem. Now let’s consider the other side of the equation – inventory. Have you ever had material go to the outsourcers that seemed to have a life of its own – I mean it didn’t seem to want to come back. “I know we said this week, but one of the guys is off and now it will be next week”. Even small jobs at the outsourcer can have major implications if it stops an otherwise completed job from being shipped. Of course we will pick that up in our buffer holes and in terms of the measure of throughput-dollar-days late. However, sometimes, the response is to add some additional just-in-case work to guard against this in the future. Sometimes people will also admit new work in order to keep a particular center “busy.” Sometimes the post-constraint areas aren’t always diligent about keeping work moving. We can guard against these types of incidences with our other local measure inventory-dollar-days wait. Adding or building work that may still be on-time is effectively captured with this measure. Moreover it can be applied to sub-sections of the whole process. It helps to stop the squirrels in the business from storing inventory just-in-case. Throughput-dollar-days late and inventory-dollar-days wait are the two measures that effectively allow us to monitor the stability of the system, they are local measures with implications for global feedback. Just two measures. You couldn’t wish for better could you? OK, one measure, some people can’t be pleased, but we will have to suffer two.

Global Feedback – Work Order Buffer Resizing If buffer holes begin to occur in the red zone significantly more frequently, or indeed less frequently, than our target level, we must do one of two things; (1) adjust the buffer or (2) adjust the sprint capacity. Of the two, adjusting the buffer is the most rapid and easy to do. We simply change a policy on buffer size and accept a small increase/decrease in total work-in-process and manufacturing lead time. We can also use the equivalent unit-day late measure to monitor this need. If a buffer hole trend begins to “emerge” but is not yet too troublesome, then it may indicate the erosion of sprint capacity in the system and the area of that erosion, or it may indicate some special cause – a machine or quality or operating problem. The cause should be tracked down, monitored, and rectified if possible. There are definitely cases where buffer holes arise from some innocuous change in the process, “we used a different glue because it was cheaper,” which has a downstream impact that was not anticipated, “yes but we are spending more time on removing glue marks.” Emergent buffer holes also indicate potential future constraint points. We will investigate these factors in more detail in the page on implementation details. Buffer management is the monitoring aspect of our implementation and monitoring model. It provides both local control and maybe more importantly a global level feedback into our drum-buffer-rope basic configuration. We only “tune” the basic parameters when the system behavior tells us that we need to do so. We only do what we should do, and we don’t do what we shouldn’t do. If we continue to apply our plan of attack, our 5 focusing steps, then it is unavoidable that at some stage we will elevate our system constraint so that it moves to the market. What are we going to do then? Let’s see.

A Market Constraint If the bottleneck to the system is the market, then we must treat the market as the constraint and subordinate the process constraint (now best called a control point) to the market. This means only making things that the market needs and not making the things that the market doesn’t need (overproduction). Essentially the drum is now in the market place, but we can visualize this by placing the drum at dispatch – the customers’ desired due date. In fact, that is kind of “old fashioned” thinking. Bear with me for a moment, I will try to explain this more fully. You see, the control point, our internal weakest link, isn’t really so critical anymore, in fact in the next section we will see how to operate without explicit scheduling of the control point at all. While the internal weakest link is no longer critical, it is still however central to our operation. It becomes our leverage point. It is certainly seductive to consider that our leverage point should be where we have placed the drum; that is at the customers required due date. This is, after all, the point where the internal system and the external market interact. This certainly appears to be the place where we can maximize leverage. We can easily test this in a negative sense by running a few orders overdue. We know what the reaction of the market will be. It might be better to consider this interface between the internal system and the external market as a transfer point. We will look at transfer points again in the introduction to the supply chain pages. If we step back in our logic we will find that the characteristics of our due date performance is still determined by our internal weakest link. The greater the difference between the additional capacity of the internal weakest link and the market demand; that is the amount that internal capacity exceeds external demand, the smaller the buffer and the shorter the manufacturing lead time. Conversely, the lesser the difference the greater the buffer and the longer the manufacturing lead time. It is the characteristic of the timeliness of the system that is important; this is an aspect that we will explore in further detail later on in the paradigms page. For now let’s be certain that it is the interaction of the internal weakest link within the overall system that determines this characteristic. This is where we leverage the whole system from. Once the constraint is in the market then we should do our utmost to continuously increase the market demand while increasing internal capability at the same time – so as to keep the constraint within the market and to be able to provide that market with a very high level of service. In fact our service level must be much better than anyone else – and it will be! In a market constrained environment we can continue to use drum-buffer-rope with a market drum and an internally scheduled control point, referred to as traditional drum-buffer-rope, or we can begin to use a more recent development called simplified drum-buffer-rope or S-DBR (12). Simplified drum-buffer-rope is at the heart of the Theory of Constraints make-to-stock solution – and that is where we are heading next – so let’s investigate simplified drum-buffer-rope first in our more familiar make-to-order environment.

Simplified Drum Buffer Rope Simplified drum-buffer-rope is an excellent reminder to heed the 5th step of the 5 focusing steps; don’t allow inertia to become a system constraint. When most drum-buffer-rope implementations move the constraint into the market, they continue to protect the internal process, at very the least, at 2 places, the internal weakest link or control point, and the shipping date. Do we still need to do this? The answer appears to be no when the internal weakest link is working at 80% or less of its capacity to supply the market demand. In this situation it is quite safe to roll the safety up into one global safety buffer instead of two or more. How do we schedule such a system? Well, instead of having a constraint schedule and a shipping schedule we now have only a shipping schedule with a gating process off-set by a full shipping rope length. The schedule is still loaded against the capacity of the internal constraint – available hours per day, or available hours per week, over the average manufacturing lead time, but the only detailed schedule is for shipping. More correctly the schedule is loaded against up to 80% of the aggregate capacity of the internal control point. A queue of some duration will still naturally build and maintain itself in front of the weakest link, but it is no longer scheduled. Let’s try and draw this. The drum moves to a proxy position in shipping (it is really in the market but that is a little hard to draw). We now have only one rope and one buffer. Thus simplified drum-buffer-rope allows for fewer buffers without less control and without less protection for the overall system. It also “avoids” the need to determine a detailed schedule for the internal constraint – leave it to the operators of the constraint/control point to determine their own local schedule in accordance with the shipping schedule. Buffer management is still in operation, but now there is only one buffer to manage – the shipping buffer. Avoiding the need to determine a detailed schedule for the internal constraint/control point is especially useful in situations where capacity is made of up multiple similar machines – some permanently set-up for certain ranges of jobs, some for large batches, some for small batches, and so forth. Another case might be special dependencies or set-ups. In this situation the foremen and their people will know better than any planner how best to exploit the system. Leave them to it, they know what to do. There is one further alteration to the basic drum-buffer-rope concepts that we must address and that is the buffer itself. Simplified drum-buffer-rope was initially described with a two-zone buffer; comprising a green zone and a red zone. However the authors, being pragmatists, have since revised this to the basic 3 zone buffer as we have used throughout here. So, let’s redraw the system with a 3 zone buffer. The length of the red zone in simplified drum-buffer-rope is defined as the time required for expediting a medium-sized order. The total buffer length can therefore be sized accordingly. Buffer resizing rules are the same as for drum-buffer-rope. Simplified drum-buffer-rope, although developed as a consequence of make-to-order systems moving towards the desired position of being market constrained, is an ideal system for operating a make-to-stock operation. In the following sections we will look at, firstly, a transitional system that uses full “traditional” drum-buffer-rope for make-to-stock and then, secondly, develop the argument for using simplified drum-buffer-rope – first in make-to-stock and then in make-to-replenish. Right now, however, we must introduce the concept of stock buffers.

Stock Buffers There are two places where the supply chain interacts with manufacturing – at the beginning of the process and at completion. It isn’t much good to have excellent on-time service for make-to-order if we don’t have the necessary raw materials on hand to begin with, nor is it very useful in make-to-stock if we don’t have sufficient resultant stock-on-hand at all times. Often we don’t even think of raw materials (or inwards goods) and finished goods as a part of the supply chain, they seem, and in fact they are, integral to the manufacturing process. But by their very nature they are the end of one supply chain, and the beginning of another. In supply chain each and every node for each and every stock becomes its own buffer. Each node must contain sufficient stock to meet demand (and variation in demand) during the period from the beginning of one re-order/resupply cycle to the next. In both make-to-order and make-to-stock environments a stock buffer for raw material input occurs at the beginning of the process. In manufacturing make-to-stock environments a stock buffer for finished goods output also occurs at completion. Let’s add these stock buffers to our diagram. We recognize that in drum-buffer-rope make-to-order both the constraint buffer and/or shipping buffer are unique in that they are measured in units of time. In contrast the raw material stock buffer and the finished goods stock buffer are measured in units of quantity. This is an interesting and important distinction, although they are defined on the basis of time, they are measured in units of quantity. We will look at this in more detail in a moment, and then we will continue to examine the concepts that we must apply to finished goods in make-to-stock and make-to-replenish. Later we will return to look at how we can modify these same concepts to accommodate the more exacting case of inwards goods in all of these environments.

Why Then Is The Stock Buffer Size & Activity Determined By Quantity? To understand why we buffer these stock positions just prior to, and just after, manufacturing in units of material rather than units of time we need to consider how we determine both the stock buffer activity and the stock buffer size. Let’s examine stock buffer activity first. Why do we use units of material for stock buffer activity when we use units of time for the constraint, control points, and shipping buffer activity? The reason is that now the number of units is invariable but the amount of time or demand that they “cover” is variable. Sometimes demand is high and we utilize the same number of units faster and over a shorter period. Sometimes demand is low and we utilize the same number of units more slowly and over a longer period. But a unit is still a unit. Thus; At a stock node a unit is a unit but the hours may differ The selection of units of time or units of material isn’t arbitrary; it is based on the nature of the step we wish to protect. If time is invariant (an hour is an hour) then we use time. If quantity is invariant (a unit is a unit) then we use quantity. Maybe it is much simpler to say that; We protect stock with a stock buffer What about the stock buffer size then? The unique perspective brought about by the designation of a stock node allows us to define the length of the stock buffer in time. Essentially the buffer is sized and “sees” a duration that extends through one period of the re-order/resupply cycle. However, the buffer now “sees” uncommitted demand – we can not tell how much we will sell in the next hour or the next day or whenever. Therefore, we must once again substitute “non-variable” units of stock in place of the variable amount of time or demand that they “cover.” Thus the replenishment buffer size is also measured in units of quantity. We will return to the part the re-order/resupply time plays in determining the stock buffer size soon.

Transition to Make-To-Stock In many make-to-stock systems it might be inelegant but never-the-less quite effective to give make-to-stock orders a due date for completion as we would in make-to-order jobs and to therefore operate a shipping buffer. The shipping buffer then supplies a stock buffer. The stock buffer itself will also contain some measure of safety. This undoubtedly overdoes the safety aspect, as we have system safety in safety stocks and we have system safety in the shipping buffer. However, so long as this is recognized, and so long as it works, then it shouldn’t be a problem. Let’s have a look at such a re-order system using full drum-buffer-rope. Most often the stock levels in the stock buffer of this system will use some variation of a minimum/maximum or re-order point system. Stock tends to be made “as needed” in set quantities (batches) or multiples of these set quantities. This is a fixed-quantity variable-frequency re-ordering system. Very crudely we could think of this as filling irregularly but completely from the bottom towards the top of our stock buffer. In traditional reorder approaches to make-to-stock using min/max systems we can “wing” a reorder system using recent rates of consumption to give us a “time remaining until order release.” Orders with the least time remaining get priority. Priority = [(Stock-On-Hand + Work-In-Process – Orders) – Reorder Point] / Consumption Such an arrangement of course also allows for a very effective mixed make-to-order/make-to-stock operation. Probably the greatest majority of businesses currently operate in this manner. This transitional example is shown with an internal constraint. In this case we can try and supply all of the demand but it is not possible. Therefore, either we must accept that some of the stock buffers will be out-of-stock some of the time, or we must reduce the total number of stock items – just as we must turn down some orders in an internally constrained make-to-order system. Despite the protests of purists, make-to-stock systems often start from this position but it isn’t so easy to recognize. In an internally constrained make-to-order system the turning down of orders is often active ; we make a decision to do so. In an internally constrained make-to-stock system the decision is sometimes passive ; the customer makes it for us. Moreover the customer may never “voice” disappointment at a stock-out, they may simply go elsewhere and we might not ever know. Really, we want our initial internally constrained make-to-stock operation to move towards being market constrained. Think about it. We need sufficient stock so that our customers can always find what the want, and when they want. Such market constrained systems tend to be replenishment systems which we will discuss in a moment. The objective of this transitional and traditional drum-buffer-rope stage is to make available the needed capacity to get to the required level of output. The identification, exploitation, and subordination steps of full drum-buffer-rope will uncover existing capacity that was previously unrecognized. We need to understand why this transitional stage is so common. One reason without doubt is that materials requirements planning (mrp) and material resource planning (MRP II) use time as the default for treating stock orders. This is so common that we don’t even challenge the assumptions behind it. To do so however, we need to introduce a new concept. We need to introduce the concept of replenishment stock buffers.

Drum-Buffer-Rope & Make-To-Replenish As we move from being internally process constrained, to being externally market constrained – a normal consequence of implementing drum-buffer-rope – then there is an opportunity to move from fixed-quantity variable-frequency make-to-stock to something superior. We will call this superior mode of operation make-to-replenish – as distinct from make-to-stock. First, however, we need to be aware of a subtlety. We need to remind ourselves of our plan of attack, our 5 focusing steps. They are; (1) Identify the system’s constraints. (2) Decide how to Exploit the system’s constraints. (3) Subordinate everything else to the above decisions. (4) Elevate the system’s constraints. (5) If in the previous steps a constraint has been broken Go back to step 1, but do not allow inertia to cause a system constraint. In other words; Don’t Stop . We know that the constraint is in the market, so where then is our leverage point? Using the terminology of Dettmer (13) it has to be somewhere within our span of control rather than our sphere of influence. Just as the due date was a seductive place to consider leveraging from in make-to-order, so too here is the finished goods stock. After all, we must have the right material in the right place at the right time – always. However, just as with due dates in make-to-order, the greater the difference between the additional capacity of the internal weakest link and the market demand; that is the amount that internal capacity exceeds external demand, the smaller the queue in front of the weakest link and the shorter the manufacturing lead time and thus the smaller the stock buffer that is required. Conversely, the lesser the difference the greater the queue in front of the weakest link, the longer the manufacturing lead time, and thus the greater the stock buffer that is required. Again, it is the characteristic of the timeliness of the system that is important and it is the interaction of the internal weakest link with the overall system that determines this characteristic. It is from the internal weakest link that we leverage the whole system. Does that seem logical? Then let’s move on. In a make-to-replenish environment, stock provides the system safety against both the variation in demand from the customer and variation in the time to needed to re-order and resupply the stock. Therefore we don’t need to use a shipping buffer; the stock is our buffer. This is an ideal system, let’s see how it looks, let’s draw this new system. This is a replenishment system. It is no-longer internally constrained, the drum has therefore moved to the market. The finished goods replenishment buffer is the sole buffer in the system for each stock unit that we produce. At a pre-determined frequency; hourly, daily, or weekly, we must check and raise a material release order sufficient to replenish the buffer back to its full level. This is a fixed-frequency variable-quantity replenishment system. Very crudely we could think of this as filling regularly but somewhat incompletely from the top towards the bottom of our replenishment buffer. Replenishment buffers characteristically oscillate between being nearly full and being somewhat empty. They oscillate between zone 3 and zone 2 – rarely do they move into zone 1. Movement into zone 1 is a signal for management action (action by exception). If having 1/3rd of your stock “sitting” in zone 1 sounds wasteful remember that this system is frequency driven. Total finished goods stock may well be 1/2 or 1/4 or even less of that previously required at this first stage after manufacturing. Let’s check that we understand why. In the earlier discussion on drum-buffer-rope make-to-order we limited ourselves to the benefits that occur when excess work-in-process is removed from the system, we “dried the system out” by at least 50%; and lead time was reduced proportionally. We didn’t consider the “what if” when an order consists of 1000 units of the same thing, surely we could manufacture that as two lots of 500? In a way we would have been getting ahead of ourselves, in fact the whole subject of batching is so important that there is a further page on it called “batch issues.” However, because batching is endemic to make-to-stock we need to address it here to some extent. So let’s ask the what if. What if we have a batch of stock of 1000 units, and what if we made two smaller batches of 500 units instead? Well, each batch would move through the process twice as fast as before. The rationale is that most of the time that a batch spends on the floor is time spent waiting, and therefore a batch of 500 waits half as long as a batch of 1000. Unconvinced about the waiting? Go attach a balloon to a batch or two and see what they are doing over a couple of days. So, if we dry the system out of excess work-in-process and that reduces lead time by at least half, and then we reduce batch size in-turn by half that will reduce lead time to a quarter – without even trying. Of course we have to run those batches twice as often, could that be a problem? Well not usually. It is not a problem as long as we don’t turn a non-constraint into a constraint by too many set-ups. Now let’s look at two further considerations. Firstly; we don’t batch when we replenish! Oops. Trying not to use the term “batch” from now on is going to be so darn difficult, however, it is not politically correct to talk about fixed-batches in replenishment because they don’t exist, therefore we will talk about the variable replenishment aggregate size instead. As Schragenheim points out, in all of the standard approaches to make-to-stock there is a buried assumption somewhere as to a fixed batch size (8). We assumed exactly that in our transitional approach above. So yes, common sense tells us that we must aggregate some of the stock units some of the time, least we grind the system to a halt, or we are Toyota, or we are very clever. However, we should strive to keep the size of our replenishment aggregate as small as possible and variable; that is, not invariably the same size each and every time. Secondly; we may have product cycles. Sometimes when the nature of the material is different for different stock we may wish to make everything required with one material before we change to another. Or we might have a preferred sequence of set-ups either up or down the size range or some other set of dependencies that cause us to cycle through our manufacturing process. The good news is that cycle times will decrease in proportion to our variable replenishment size reduction. Why is this so important? It is important because our initial stock buffer size, and hence the physical inventory and financial investment that we must carry, depends upon the resupply time . In the absence of product cycles and large variable replenishment aggregates the manufacturing lead time determines the resupply time . In the presence of product cycles, the time for the completion of a cycle plus the manufacturing lead time determines the resupply time . There is a more detailed explanation of the characteristics of replenishment in the second part of the replenishment page in the supply chain section. Be aware that manufacturing professionals tend to misunderstand replenishment at first unless they have been exposed to supply chain management. If we have only ever been exposed to fixed batches of material then everything tends to look like a fixed batch of material. This is a very real and substantial block. Please check the replenishment page to test your understanding. During the implementation phase of simplified drum-buffer-rope in a make-to-replenish environment the removal of fixed batch sizes and replacement with smaller more frequent variable replenishment size orders is the critical parameter in determining system behavior.

Local Planning In Make-To-Replenish If the demand in make-to-replenish is uncommitted, then how do we plan? Well, the same way as in make-to-order simplified drum-buffer-rope, we load replenishment orders against our aggregate capacity – our daily or weekly capacity multiplied by the material release rope length. The capacity is defined as that of the weakest link in the process. We shouldn’t fill more than about 80% of that capacity over any medium term plan. Let’s add this to our model. We will see prioritization rules for loading orders in the discussion on local control. Although stock order prioritization is a local planning action rather than local control, we need to first understand buffer status in order to understand make-to-replenish more fully than we do at this point.

An Initial Finished Goods Stock Buffer Sizing Rule The amount of stock that we must hold in our stock buffer depends upon the amount of time that it must “cover” for. The principle components of that time are the reorder time and the resupply time. We can define the stock buffer as follows; Finished Goods Stock Buffer = Average Demand x (Average Re-order & Re-supply Time)

+ A Margin of Safety Because we use averages for demand and for time we use the margin of safety to accommodate that which we know least well, the variation around the average values. If the resulting buffer is too big it will soon become apparent and we can, in fact we must, resize it. Likewise, if the buffer proves to be too small then we will also have to resize it. Re-order and resupply time are clearly the most critical components. We have already discussed the factors that lead to a reduction in resupply time, most of which are within our own control; what then are the issues that determine the re-order frequency? Policy of course! Whose policy? Our policy! Yep, there are not a whole lot of excuses why re-order, which is totally under our own control in our own system, can not be substantially reduced in duration. If we are big enough to have a real problem then we will have a computer and that can easily be interrogated more frequently. If we are small enough to have no problem then we just need to do the sums more frequently.

Buffer Management – Make-To-Replenish In drum-buffer-rope make-to-replenish, buffer management once again provides us with both local control via buffer status and global feedback in the form of buffer resizing via longer term trends in buffer behavior. The model is the same; the terminology is modified to suit the environment. Let’s then look at how we determine buffer status, stock order status on the floor, and new stock order release priority. Then we are in a position to evaluate the longer term performance measures used for buffer resizing.

Local Control – Stock Buffer Status Stock buffer status is defined in the same way as a time buffer; Stock Buffer Status = (Buffer Quantity – Stock-On-Hand) / Buffer Quantity We have already used this concept with buffers for work orders in make-to-order, exactly the same principles apply for stock buffers in make-to-replenish. Let’s illustrate the concept with a few diagrams for stock buffers. The following buffers contain varying amounts of stock-on-hand (SOH). Buffer A has been depleted by 58%, buffer B by 75%, buffer C by just 17% and buffer D by 50%. Clearly the buffer status of buffer B indicates that we should investigate and if necessary expedite any in-process stock orders. Of the other buffers; C is in the green zone, zone 1, and of no concern, buffers A and D are within the yellow/orange zone, zone 2, and we should leave well alone unless we once again wish to tamper with the system – and Deming taught us what happens when we allow that occur. So let’s not do it. By using buffer status we can prioritize different product buffers amongst each other regardless of whether one replenishment buffer covers a period of 2 months and 2000 units and another replenishment buffer covers just 2 days and 20 units.

Local Control – Stock Order Status In make-to-order the relative priority of released work orders can only be affected by internal “things,” however, in make-to-replenish the relative priority of stock orders can also be affected by external changes in the market demand during the processing time. Because stock orders should be smaller and more frequent it is also possible that more than one order for identical stock can be in-process at the same time, this too must be taken into consideration. Thus the stock order status for material on the floor is (8); Stock Order Status = (Buffer Quantity – Stock-On-Hand – WIP Ahead) / Buffer Quantity Let’s illustrate this with two different stock items – “light blue” and “lavender.” The buffer status of lavender is 84%, it is well into the red zone, and we should therefore ensure that the lavender stock order #1 has priority over other stock orders such as the light blue stock order #1. What about lavender stock order #2 then? This stock order with a status of 50% still has priority over light blue stock order #2 at 25% but the only place that we might exercise this is at the slowest point if both orders are waiting in-queue. The status of released stock on the floor is important and can change over the term that it is in-process, however what we should do about this depends; it depends on the status of the finished goods buffer itself. As long as the finished goods buffer status is not in the red zone we should leave the process alone.

Local Control – New Stock Order Release Priority Buffer status tells us about the end condition of the system and whether to check or maybe facilitate work already released for production. However, without customer due dates as in make-to-order jobs, how can we prioritize new jobs for material release? To do this we must replenish the buffer to its full capacity allowing for current stock-on-hand and current work-in-process, thus; Stock Release Priority = (Buffer Quantity – Stock-On-Hand – Total WIP) / Buffer Quantity For the example that we just used with “lavender” and “light blue” finished goods stock buffers, we have the following release priorities. Both the light blue and the lavender stock have a release priority of 9%. In a replenishment environment release priorities are not likely to become very high, it is the relative value not the absolute value that is of most importance. The actual amount of material released to replenish the buffer will be the amount required to bring it be back to the full buffer quantity; Stock Replenishment Quantity = Buffer Quantity – Stock-On-Hand – Work-In-Process When demand is generally high, then the time between one stock order being released and the next order for the same stock will increase because each order is now larger and takes longer to process, however overall system set-up will decrease as a consequence. Conversely, when demand is light, the time between each order will decrease because each order is now smaller and takes shorter time to process. Overall system set-up will increase as a consequence. Schragenheim argues persuasively how this mechanism self-adjusts to load and can be used to drive system stability (8). Do you notice something important? We have moved away from fixed batch sizes and away from the fixed schedules of transitional make-to-stock. The re-order frequency and resupply frequency and quantity are now totally determined by the characteristics of the system that we implement and the demand characteristics of the customers. This is make-to-replenish. In fact we are not entirely free of fixed schedules. In replenishment we have at the very least; rechecking, re-ordering and resupply. The re-ordering and resupply are indeed free from fixed-frequency scheduling but there is one last hold-out – the rechecking. Most likely we will still batch this in time – to once a day, towards the end of the day maybe when we “know” what we have sold that day.

An Alternative Stock Buffer Sizing Rule There is an alternative stock buffer sizing rule for finished goods stock buffers. Schragenheim and Dettmer use the term “emergency level” to define zone 1; they suggest that zone 1, the red zone or emergency level, should contain sufficient stock to cover the time required to expedite a medium size replenishment order through the system (14). Three times this value will therefore define the full buffer size. Let’s make sure that we understand all of the terms that apply to a stock buffer, let’s use the light blue buffer as an example. The stock buffer is all the finished goods material on-hand, plus all the work-in-process for that same stock, plus whatever amount we have depleted since our last order release. Yes, it circular logic, but it works. Buffer status is a measure of the depletion of our stock-on-hand versus our full buffer. The red zone, or emergency level, is an indication that we could deplete our buffer and cause a stock-out unless some special, but not heroic, actions are considered and if necessary put in place.

Local Control/Global Feedback – Stock Buffer Zone 1 Penetration A make-to-order system has just one, or maybe two, buffers – either the shipping buffer or a constraint buffer and a shipping buffer. In essence the system must always have a drum buffer. Make-to-stock therefore seems overwhelming because every stock buffer becomes a drum. Thus we have move from just one or two buffers to maybe many hundreds or even thousands of buffers. Nevertheless we need to address both the aggregate behavior of the buffers and their individual behaviors. A buffer hole occurs whenever there is an incursion into zone 1, the red zone, of the buffer. Let’s draw this for a stock buffer. Tracking the aggregate incidence over all the buffers will tell us something about the system and the market as a whole, tracking the incidence in individual buffers will not tell us so much. Unlike make-to-order where each job is discrete and a buffer hole can be quantified when the job is finally completed, make-to-stock is continuous – we continuously replenish and customers continuously deplete our replenishment. So we can not put a mark in the ground that says “hole closed on this day.” Instead we must continuously monitor the number of units missing and the number of days that they are missing from zone 1. This is the measure that will tell us what is happening in individual buffers. We must record the number of units “missing” from zone 1 each day that any units are missing and we must aggregate this over our reporting period – be that a day, a week, or a month. The measure is unit-days late.

Local Control/Global Feedback – Stock Buffer Performance Measurements From the buffer hole data we can construct a more meaningful measure. One that includes the value of the problem. In a for-profit environment these are known as throughput-dollar-days (late) and inventory-dollar-days (wait). Let’s add these to our diagram. I prefer to add “late” and “wait” to these measures because to those who are unfamiliar with the terms it is hard to know what they mean or their significance. And these two measures are so important to the overall success of drum-buffer-rope that we can’t afford to lose people through obscure terminology. Throughput-dollar-days late is our unit-days of material missing from zone 1 of the buffer multiplied by the expected throughput of the product. This way both the size of the hole and the value placed in jeopardy are taken into account. We must remember that the purpose of a buffer hole is an internal warning to us that we may “ping” a customer. Throughput-dollar-days tells us how loud that ping could be – of course we will absolutely strive to remedy the problem before it reaches the customer. Inventory-dollar-days has an important role in make-to-replenish. No-longer does it just protect us from gaming the system – having no zone 1 penetrations by loading excess work-in-process; it now alerts us to the ageing of stock.

Global Feedback – Stock Buffer Resizing What if the risk of “pinging” a customer becomes more frequent and greater? Surely this must be a message that we need to increase the size of the particular buffer concerned – unless of course we want to spend all our time and energy fire-fighting (and we put our fire-fighting gear away – right?). So buffer penetrations that are too frequent into zone 1, the red zone, require an increased in buffer size. Conversely, buffers that are always in zone 3, the green zone, indicate a need for reducing the size of the buffer. We monitor the behavior of our protection and if it is insufficient, or if it is over-protective, we make adjustments to the system to accommodate this.

Drum-Buffer-Rope In Mixed Make-To-Order/Make-To-Replenish In the earlier re-order system we were able to mix make-to-order and make-to-stock by giving make-to-stock orders a due date. In mixed make-to-order/make-to-replenish we can do away with the due date for stock. Essentially we have two drums. We have two drums because we have two markets; those that can’t wait (make-to-replenish) and those that can or must wait (make-to-order). The typical situation here might be standard items (make-to-replenish) and special one-off items (make-to-order) – even though we might make several hundred or several thousand units in one male-to-order. Of course the aggregate load of the two drums can’t exceed the availably capacity of the most capacity constrained internal resource. But then simplified drum-buffer-rope allows us to load against that aggregate capacity. How does this look, let’s see. Managing make-to-order is fairly clear-cut; we have made a commitment to the customer for a future due date. What about make-to-replenish? In our explanation of replenishment buffers we noted that the replenishment buffer “sees” uncommitted demand – we can not tell how much we will sell in the next hour or the next day or whenever. So we could launch a replenishment order only to find that the demand has slackened-off, or increased, part way through the process. We would hate to sacrifice a late make-to-order job because we thought the demand for a make-to-replenish order was more urgent, only to then find out that the make-to-replenish wasn’t urgent at all – the demand had dropped away again. How can we manage such a duality? We are dealing with both time (make-to-order) and with quantity (make-to-replenish). Well, Schragenheim shows us how; with the buffers of course. Let’s have a look. Here we have 4 buffers; 2 make-to-order time buffers and two make-to-replenish stock buffers. One of each type has a very high buffers status. Which should we attend to first? The work associated with buffer 1 of course, this has consumed more of its buffer than the work associated with buffer 4. With buffer 4 of course we must check that there actually is work-in-process for this stock, and if not we must release (and in this case expedite) it. This is the power of Schragenheim’s buffer status methodology. “Even though the calculation of the buffer status is different, they are fully comparable (8).”

The Role Of Reduced Inventory in Drum-Buffer-Rope It seems almost quaint now, but in 1986 when Goldratt and Fox wrote The Race, the role of reduced inventory was still, in many peoples’ minds, an open question. Never-the-less, they proposed six competitive edge issues built around; product, price, and responsiveness, in which reduced inventory has a positive effect and set out to demonstrate their case (15). These issues are listed below. Product Quality Engineering Price Higher Margins Lower investment per unit Responsiveness Due-date performance Shorter quoted lead times Their demonstration of the case is the reason for accepting in the measurements section that inventory reduction does in fact have a positive impact via future throughput on the bottom line measure of net profit. Of course time also appears to have substantiated the importance of inventory reduction in all six cases. The objective of Theory of Constraints and drum-buffer-rope however is not to reduce inventory per se, it is to increase throughput – the rate at which the system produces money or goods or services in order to move the system towards its goal. Most often inventory reduction is a desirable and feasible outcome; but it supports the objective of increased throughput, it is not an objective itself. Making Throughput the objective in Theory of Constraints is quite different from other approaches. In the pursuit of bottom line improvements traditional cost management focuses primarily on reducing operating expense, and is neutral about inventory. Just-in-time focuses primarily on reducing inventory, and is neutral about operating expense. Constraint management focuses primarily on increasing throughput and is neutral about operating expense. We can illustrate this as follows; Traditional

Management Just-in-Time Constraint

Management Operating Expense ß Û Û Inventory Û ß ß Throughput Ý Ý Ý All three approaches; traditional management, just-in-time, and constraints management are naturally interested in increasing throughput, but only constraint management has this as its primary focus. Let’s run past this in a different direction. Just-in-time, and to some extent kaizen, are noted for actively lowering inventory in search of problems. The exposed problems are then resolved and throughput may go up as a consequence. Theory of Constraints is almost the reverse. It is noted for actively seeking out problems to increasing throughput. The problems are resolved and inventory may go down as a consequence. Problem solving via inventory reduction is well and good for work-in-process, but what about raw material and inwards good then? Reducing raw material inventory only to find the problem is with a vendor is not especially useful unless the vendor is willing to improve. Don’t let just-in-time zealousness erode protective raw material supply. Just-in-time works in Japan because the obligations of mutual interdependence are much greater. In Western businesses, if there is a particularly unreliable vendor then, at the very least, we must protect ourselves with as frequent as possible small-lot replenishment. Let’s explore this further.

Raw Material And Inwards Goods Stock Buffers Many businesses, especially make-to-stock, have standard common raw material or inwards goods items that are keep as stock-on-hand. Essentially theses are raw material stock buffers. In the introduction to raw material and finished goods buffers we remarked that raw material is a more exacting environment than finished goods. Why is this? Well, ask any purchasing officer or in-wards goods officer who failed to have the right material in the right place at the right time for production. In reality we have almost no control or authority over the external re-supply chain and yet we are expected to assume full responsibility for managing the goods within it. How do we best protect our system in this case? Let’s draw our simple raw material stock buffer feeding our production system and work outwards from there. Essentially, raw materials or inwards goods stock buffers are treated as we treated finished goods stock buffers in make-to-replenish, except we must now be more cautious not to cause a stock-out; we must take into account our real lack of control over most of the re-supply chain. As a consequence we need to re-verbalize our initial buffer sizing rule. It should now read; Inwards Goods Stock Buffer = Maximum Demand x (Average Re-Order & Re-Supply Time)

+ A Factor For The Unreliability Of The Re-supply Time The key point is that we must accommodate the maximum anticipated demand and the maximum unreliability of the vendor. We are really applying Goldratt’s first two rules of business; (1) “be paranoid,” and (2) “be paranoid.” The 3rd rule is also useful – “don’t get hysterical.” We guard against hysteria by frequent re-orders. Why? Let’s see. The replenishment time is composed of up to 4 components; (1) Our re-order frequency – and hence duration (2) Vendor cycle frequency – and hence duration if it is not ex-stock (3) Vendor process lead time – and hence duration if it is not ex-stock (4) Vendor order processing & shipping frequency – and hence duration We have control over just one of those issues, our own re-order frequency. We should make sure that this is not a policy problem of our own making. With these exceptions, we can otherwise treat raw material or inwards goods stock buffers in the same manner as we have discussed for finished goods stock buffers.

But Wait, Reality Isn’t This Simple! If this explanation of drum-buffer-rope, make-to-order, make-to-stock, and make-to-replenish seems quite simple and straightforward then that it excellent; we have an understanding of the underlying fundamentals. If experience tells us that reality is more complicated than these simple I-plant examples, then that too is excellent. Now we are in a position to better understand how to apply this methodology to more complicated V-plants and A-plants. The best place to begin to get a better idea of how to apply drum-buffer-rope to a particular situation is a set of diagrams for more complex layouts in The Race (16). The Constraints Management Handbook also deals with V-A-T issues at length (17). The whole second volume of Synchronous Management is devoted to implementation issues in V, A, and T-plants (18). The production simulator in Production the TOC Way (19) allows us to run computer-based simulations of whichever type of plant is relevant and to learn “first-hand” what we need to do in order to move towards our companies’ goal. The simulator is a tested and true approach – try it. You will gain more in two days using a properly facilitated simulator than two months of other endeavors. There is also a more recent subset of the production simulator with a slightly different interface made available as the TOC Executive Challenge (20). This is another excellent way to develop understanding in a “safe” (read virtual) environment. Stein’s book Re-engineering The Manufacturing System (21) details the algorithms for the “Haystack Compatible” scheduling system that is the more complete expression of the drum-buffer-rope make-to-order core described here. Of course Goldratt’s book The Haystack Syndrome does this as well but in a more concise way. Finally, incorporated within Warp Speed (22), Schragenheim has made available a “Management Interactive Case Study Simulator” or “MICSS” for short. This offers a rather sophisticated ERP view on a simple linear plant with the opportunity to run the plant under a number of different logistical systems (MRP, DBR, S-DBR) and to evaluate the effect of different policy changes on the outcomes. It is a very effective tool for self-study and group-learning.

Some Changes in Definitions The revised edition of the TOC production simulator and the “Insights” introductory material make some subtle changes to how we define some buffers. The older set of definitions has been used on this web page, however, a concise discussion and comparison of the two sets can be found on a short sub-page here .

Summary

Well, we finally introduced the Theory of Constraints production solution – drum-buffer-rope. A mechanism that allows us to rapidly and significantly increase output and quality, shorten lead times and work-in-process, while improving on-time delivery and stock. In a nutshell what we have done is to learn how to identify the weakest link in the production chain, exploit its current capacity, and protect it from uncertainty. And maybe, at its core, that is all that Theory of Constraints really is. Here we presented drum-buffer-rope within the context of the 5 focusing steps, our plan of attack. The fourth step reminds us to then strengthen the weakest link and the fifth step contains an important admonishment – don’t allow inertia to become a system’s constraint. In other words drum-buffer-rope isn’t just a method of identifying and protecting the weakest link, it is a process of robust, continuous, and on-going improvement. Understanding the generic solution is well and fine, but now we also do need to look at some of the necessary detail of actual implementation. Let’s do that next.

References (1) Newbold, R. C., (1998) Project management in the fast lane: applying the Theory of Constraints. St. Lucie Press, pg 149. (2) Goldratt, E. M., (1990) The haystack syndrome: sifting information out of the data ocean. North River Press, pg 146. (3) Goldratt, E. M., (1997) Critical chain. The North River Press, pg 149. (4) Stein, R. E., (1996) Re-engineering the manufacturing system: applying the theory of constraints (TOC). Marcel Dekker, pg 143. (5) Goldratt, E. M., (1990) The haystack syndrome: sifting information out of the data ocean. North River Press, pg 128. (6) Schragenheim, E., and Dettmer, H. W., (2000) Manufacturing at warp speed: optimizing supply chain financial performance. The St. Lucie Press, pp 123-135. (7) Schragenheim, E., and Dettmer, H. W., (2000) Manufacturing at warp speed: optimizing supply chain financial performance. The St. Lucie Press, pg 176. (8) Schragenheim, E., (2002) Make-to-stock under drum-buffer-rope and buffer management methodology. APICS International Conference Proceedings, Session I-09, 5 pp . (9) Goldratt, E. M., (1990) The haystack syndrome: sifting information out of the data ocean. North River Press, pp 206, 209, 241. (10) Caspari, J. A., and Caspari, P., (2004) Management Dynamics: merging constraints accounting to drive improvement. John Wiley & Sons Inc., pg 180. (11) Goldratt, E. M., (1990) The haystack syndrome: sifting information out of the data ocean. North River Press, pg 136. (12) Schragenheim, E., and Dettmer, H. W., (2000) Manufacturing at warp speed: optimizing supply chain financial performance. The St. Lucie Press, 342 pp. (13) Dettmer, H. W., (1997) Goldratt’s Theory of Constraints: a systems approach to continuous improvement. ASQC Quality Press, pg 67. (14) Schragenheim, E., and Dettmer, H. W., (2000) Manufacturing at warp speed: optimizing supply chain financial performance. The St. Lucie Press, pg 177. (15) Goldratt, E. M., and Fox, R. E., (1986) The Race. North River Press, pp 36-67. (16) Goldratt, E. M., and Fox, R. E., (1986) The Race. North River Press, pp 96-105. (17) Cox, J. F., and Spencer, M. S., (1997) The constraints management handbook. St Lucie Press, pp 101-128. (18) Umble, M. M., and Srikanth, M L., (1997) Synchronous management: profit-based manufacturing for the 21st Century. Volume 2 implementation issues and case studies. The Spectrum Publishing Company, 234 pp . (19) Goldratt, E. M., (2003) Production the TOC way (revised edition). North River Press. (20) Tripp, J., (2005) TOC Executive Challenge: a goal game. The North River Press Publishing Corporation, 56 pp. (21) Stein, R. E., (1996) Re-engineering the manufacturing system: applying the theory of constraints (TOC). Marcel Dekker, pg 190. (22) Schragenheim, E., and Dettmer, H. W., (2000) Manufacturing at warp speed: optimizing supply chain financial performance. The St. Lucie Press, 342 pp. This Webpage Copyright © 2003-2009 by Dr K. J. Youngman