A Guide to Implementing the Theory of Constraints (TOC)





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How Can We Characterize Marshalling?

There is an inverse to the distribution supply chain, it’s called marshalling.  In fact, the diagram below is the same one that we used for distribution, turned on its head, and reworded for log marshalling.  Marshalling is a convergent supply chain from production to end user, and although not as common as a distribution network it is still critically important to a number of industries.  Moreover, many of the same opportunities that we identified for distribution can also be found in marshalling.

Processing of patients in a public health system should also be a marshalling operation, but currently it isn’t.  We will address this in the next page after examining log marshalling in detail.

If you have arrived at this page directly rather than sequentially through replenishment and then distribution, please consider reading the replenishment page first.  This will ensure your understanding of the technical solution (the planning and control system) that we are going to apply to these two particular situations.  Forearmed with such knowledge you will be in a much better position to evaluate the description of the current problem and also the potential for the detail of the solution.

Marshalling is characterized by multiple source nodes which produce a range of products that have some commonality with one another.  Therefore we can obtain the same type of end product from various combinations of source nodes.  The product is then on-sold or moved through a converging supply chain to an end user.  The end user node at the convergence point is often characterized by “lumpiness” or infrequency (but not necessarily irregularity) of the operation.

A single supply node can’t supply the end user demand by itself; moreover, the rate at which the end user can consume product is much greater than the rate at which all the source nodes can resupply.  Therefore there is a continuous resupply going on to refill the end user node.

The other levels in the system don’t manufacture anything.  They may purchase and on-sell or they may simply reflect storage or a change in mode of transportation.  And although they don’t have a manufacturing lead time, the do have a resupply lead time.  Broadly speaking the resupply lead time can be characterized as the time taken to load, transport, and unload the products from one node to another.

The time taken between production at the source node and consumption/purchase at the end user node can be quite considerable – multiples of months or maybe quarters.

If we apply our knowledge of production systems to supply chains and recognize the current pervasive mode of operation – the reductionist/local optima approach as we described in the pages on measurements, people, and process of change – then we could well expect local efficiency measures to be paramount in marshalling.  We can test that by asking ourselves whether we can in fact currently operate the system in order to have just the right quantity of just the right material, in just the right place, in good time – always.  If not, then we know there is room for improvement.

Let’s examine a specific case in more detail – log marshalling.

Log Marshalling

The system as drawn above is a generic representation of a regional export log marshalling operation (regional in a non-continental sense).  The marshalling chain is an integrated business, harvesting trees, making logs, and transporting them for sale and pick up by ship.  Until the end user has made a purchase and the logs are loaded onto a ship the system hasn’t made a sale.  The system could also be considered as a local rather than regional log supply system in which case there would also be aspects of distribution involved.  However the marshalling part would still look very similar we only need change some of the words, let’s do that.

This local diagram if you like is an exploded view of one arm of the previous regional diagram covering the section from forest staging to the railhead.  Here the railhead is replaced by the log warehouse.  The super skid is either a log factory or a log yard where raw logs are graded and cut for sale.

Log production (growth) has an interesting characteristic, it doesn’t stop.  Trees continue to grow and mature regardless of whether they are harvested or not.  Plantation trees are grown with a specific harvest age in mind.  Volumes are large, one arm of the regional diagram for instance currently produces about 2500 truck & trailer loads of produce a week.

Log Making

Log making and log harvesting are sometimes combined, and sometimes separate.  Log making has the same properties as a factory node in a manufacturing system.  In fact log making is a small “V-plant.”  We looked briefly at the characteristics of V-plants in the page on production and again in supply chain distribution.  One of the main characteristics of V-plants under local optima operation is “stealing,” in this case issuing cutting orders for one thing but then re-cutting it up to fulfill something else.  As we all know we can re-cut a long log shorter, but we can’t uncut a short log.  Let’s draw this V-plant assuming one forest gang is filling one forest staging node from the diagram above.

In this simple model the raw log goes through a series of divergent points (class, length, grade).  The end product is a range of “finished goods” stored and awaiting transportation to the next step in the marshalling supply chain.  If the logs were cut to order, how then could “stealing” be in operation?  Quite simply in fact – new orders are issued to countermand the ones previously issued.  The question then arises why are new cutting orders issued?

In New Zealand forest blocks are cut-to-order (1).  For cut-to-order, read cut-to-forecast, and in this case the forecast horizon is 3-4 months from the time of cutting to the time of shipping.  Why do we need to cut so far out from final sale?

This long lead time arises from the fact that it takes a lot longer to harvest, transport and pre-position the loads in the port-side marshalling yards than it does to load-out from the port-side marshalling yards to the ship.  Ships mostly like to load-out at about the same time – towards the end of the month and as quickly as possible.  The purchasing company also likes to leave finalizing the shipload until the last moment.  As you can see the problems are quite similar to distribution, and the solutions, as we will see, are the similar as well.

Currently, then, we might expect that sometimes we miss maximizing a sale (quantity and grade and size requested) and hence the return to the owner.  In a system which is not internally constrained by production, failing to maximize a sale should be a crime.  We need a plan of attack.

Plan Of Attack

Our plan of attack should be starting to look very familiar – it’s our 5 step focusing process once again;

(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.

What is the constraint in this system?  The limited number of customers who want to buy product from us.  Once again that almost answers the second question – how to exploit the constraint?  We need to ensure that we can always meet our customer demand and not miss any sales, nor sell something of less value than the customers’ original desire.  We will need to deduce the mechanism to best exploit the constraint.  We will also need to develop how to best subordinate the system once the exploitation strategy is in place.

To enable us to determine the exploitation and subordination tactics we need to examine the properties of log marshalling networks in a little more detail.  Let’s do that.

The Log Marshalling Network

Let’s take a line item – a grade and size and quantity of log.  What is the accuracy of the forecast for demand 1 month from shipping?  Plus or minus 5% maybe?  Sometimes we could sell 105% of the current demand and sometimes we might sell 95% of current demand.  What then is the accuracy of the forecast 2 months from shipping?  Plus or minus 10% maybe?  Sometimes we could sell 110% of current demand and sometimes we might sell 90% of current demand.  How about 3 months then?  Probably the accuracy for a line item 3-4 months out is more like plus or minus 20% percent of current demand.  Even if we can determine the aggregate demand for all line items right on the nail (plus or minus 5%) we will still have too much of some line items and not enough of some other line items.

So, just as in distribution, the accuracy of the forecast detail degrades with the length of our forecast horizon.  The aggregate demand might also alter as well over longer forecast periods.  There are two unavoidable consequences of this.  The first is that we will also have missed some sales.  The second is that will have some stock that sits on the wharves for several months – stock that was paid to be cut and transported and which we haven’t yet sold.  Logs, however, are a perishable product.  They develop fungal sap stains within a short period of time.  At that time they become “pulp” logs for pulp and paper production and are downgraded.  Then a prime log has to be re-transported back down the system for sale at a much, much, lower margin.  This is similar to meeting your “use-by-date” in distribution and just as expensive in terms of opportunity lost.

Let’s call this suite of problems “forecasting error.”

However, in addition to forecast variation we have another factor – supply variation.  Supply variation occurs because we try to cut whole blocks according to some predicted yield of grade and size and quantity that are a best match to our forecast demand.  We can’t cut half a tree down, and under plantation management regimes we can’t cut 60% of a block and leave the remainder standing.  Therefore, it isn’t so difficult to produce too much of some items and not enough of others, regardless of the forecast that we are working to.

Therefore we miss sales in two ways;

(1)  Forecasted demand variation.

(2)  Actual supply variation.

So this is a little different from pure manufacturing where we do have the option of only making what is required (but try telling manufactures that; manufacturers like making things regardless).

We end up using valuable capacity to cut trees that we don’t sell (we guessed that they were the right trees to cut when we cut them), and then we feel as though we don’t have sufficient capacity to cut the trees that we are selling.  It’s a bit like the factory source node in distribution, even though the aggregate sales might be level at retail, the demand level at the factory can be quite lumpy – and although overall there is sufficient total capacity we don’t have sufficient peak capacity to handle the induced peaks in demand.

To this mix we must add one more component – the tendency towards local optimization and efficiency measures.

In the distribution supply chain we used the analogy of a V-plant in manufacturing to explain some of the characteristics of diverging systems.  Fortunately because marshalling is convergent – a big funnel directed at the wharves in the regional model or the log warehouse in the local model – we don’t quite have the same problems.  For marshalling the correct analogy from manufacturing is an A-plant.  Under traditional management practices in A-plants the tendency is to misallocate resource time in an attempt to maximize efficiency and utilization figures.  Large batches are used to keep the measurements high resulting in a poor component mix and constant shortage of the right parts (2).  Furthermore these large batches move in waves throughout the plant causing temporary bottlenecks to wander from resource to resource.  Since material is constantly out of balance, overtime is used to ‘catch up’ so that shipments can be made on time (2).  We can expect similar things to happen in a marshalling supply chain.

Unlike distribution where the consumer demand must be aggregated back to the source node, in marshalling the consumer demand must be disaggregated back to the source nodes.  The effect however is the same, in addition to forecasting error due to time, we have an error due to improper disaggregation or allocation, that is to say our predisposition to local efficiency will cause us to do more log making and making of particular grades in some places (because it is deemed to be locally efficient) and less in other places (because it is deemed to be locally inefficient) at any one time than pure disaggregation of market demand would have required.

Let’s call this “allocation error.”

Now we can understand better why instructions to cutting crews change even though we are dealing with long lead times and steady demand.  We have in our supply chain both multiple dependencies (layer to layer) and considerable room for variation from;

(1)  Forecasted demand variation.

(2)  Actual supply variation.

(3)  Local efficiency drivers/allocation error.

We can’t do anything about forest supply variation.  In an open market system, pricing reflects the difference between real supply and real demand.  However we certainly can do something about forecast variation, and we certainly can do something about local efficiency based drivers.


We have been calling the supply chain described here a marshalling supply chain.  However, a more accurate description would be marshalling and consolidation.  Consolidation according to harvesting production-push.  The production-push is signaled by a forecast about the future based upon recent past trends and intuition about the near future.  This system is called cut-to-order but in reality it is cut-to-forecast.

In the Theory of Constraints manufacturing application, drum-buffer-rope, we saw how work is pulled through the system by the constraint schedules.  In the same way just-in-time also pulls work through the system.  We need to invoke the same principles here in our marshalling system; we need a consumer or customer demand-pull.  That way we produce a mix that most closely matches what is required by the system to satisfy demand.  However, how can we do this when we have such long resupply times?  Well, what if we were to consider each layer of nodes in the chain one at a time rather than the whole chain from beginning to end?  What if we were to consider resupply to just the next level?  Then lead times would be much, much, shorter.  Surely that would help.

Well, we have already described such a situation in the previous page on distribution; we have seen how to replenish using replenishment buffers in such a fixed-frequency supply chain system, so we really need to invoke the same mechanism here – pull-to-replenish.

We are going to move from harvesting production push-to-forecast to customer demand pull-to replenish.  However, before we look at the solution there is one more aspect to consider.

We Can’t Sell Half A Tree

Fortunately this is not a problem that is unique to forestry it common to all primary and extractive producers.  If you remove the milk fat and milk protein from milk to make cheese you had better have a good market for the milk sugars that are left behind or a good market for the industrial alcohol that you can manufacture from the milk sugars.  If you extract the milk fat for butter then you had better have a good market for the skim milk powder that you can manufacture from the remaining milk.

In forestry the yield, the throughput for the whole tree, determines the yield for the system.  How do we determine the yield?  Well, applying throughput analysis to each product stream would be a very good start.  Essentially we are interested in the contribution margin that each product stream can yield, rather than some measure of net profit per product.  In other words we are interested in determining significant variable costs but not in allocating indirect overhead or direct labor.  It is almost certain that under such analysis the order of sales preference (to maximize cash inflow) will change.

Mining As A Solution

Hard rock gold miners of yesterday and today also operate a type of marshalling supply chain.  Ore of various grades occurs in various veins and even within the same veins in different places.  The greatest total yield from the mine comes from ore-grade management.  This approach may be viewed as keeping the high-grade ore diluted with lower grade ore, or keeping the lower grade ore bulked up with high-grade ore.  How do we extend this to forestry?  Basically we may have to accept some mobility between forest blocks in order to maintain proper yields – the yields that the market is willing to pay for, or pay the most for overall.  We are cutting trees to make money after all.

General Solution – Log Marshalling With Replenishment

We have identified the leverage point; sales to the customer, and we have identified the exploitation strategy – customer demand pull-to-replenish, but how do we go about it?  Well, we will use the Theory of Constraints supply chain solution – replenishment.  If you are unfamiliar with fixed-frequency variable-quantity replenishment then please check the explanation on the replenishment page – it is important.

The demand is determined at each stage in the supply chain and buffered accordingly.  So where do we start?  Probably the best place to start is the place where the aggregate demand is best known and in a marshalling system this is at the convergence point, the port-side marshalling yards in the regional model or the super skid in the local model.  At all it is at his point that we have the greatest quantity and variety of source material (uncut logs) and the greatest range of products to fulfill.

We need to determine for each stock item the size of the buffer that will protect us adequately against customer demand over the period of reorder and resupply for that item from the next node up in the system.  In fact the log warehouse in the local model is the buffer for the super skid.

What about the next node or nodes up, how do we treat them?  Just the same, the aggregate buffer size must be sufficient to supply the next node down over the period of reorder and resupply for that item from the next node up in the system.  As we move back up the system from port-side to forest staging in the regional model or log warehouse to forest gang in the local model we must size the buffers needed to ensure continual supply.  We no longer need to forecast.  We may still need to issue cutting orders – but only to instigate replenishment the forest staging buffers and no more.  Let’s summarize this.

Introducing replenishment buffers and increasing resupply frequency automatically
 aligns the process with the goal

We have moved the protection for the system to the best possible area – portside/super skid.  This is the area with greatest aggregate supply and greatest aggregate demand and in this instance it is also closest to the customer.  As we move back up the system and the nodes become more disaggregated then we run a risk that our safety portion of the buffer may become too large.  However, we can reduce this impact by increasing the frequency of transportation at the most critical level (forest staging) to reduce the overall size of the buffers in the positions between the forest gangs and the first major consolidation.  We may also need to increase the frequency of issuing cutting orders, and increase the flexibility for gangs to mill concurrent blocks according to actual yield demands – as determined by buffer consumption.

We previously used forecasting because there was a considerable time delay between the producer and the consumer – caused in part by the inventory.  Now we can resupply just to the next node based upon most recent need.  The time delay we are dealing with is from one node to the next, not from the beginning to the end of the process, therefore we don’t need detailed forecasting anymore.  In essence we have synchronized the whole supply chain by buffering each node against the supply and variation in supply leading into that node and the demand and variation in demand leading out of it.  In fact each node is a buffer.

In the previous page on distribution we saw how General Motors had succeeded in replenishing Cadillac dealers in Florida for a very broad range of popular custom configurations (if you are buying a Cadillac you get to determine not only body and color, but also engine, transmission, and numerous other options that on most vehicles are fixed).  In the past it was taking a dealer 6-10 weeks to pull a replacement car from the factory direct.  With replenishment to a regional distribution center dealers can get a replacement within 24 hours in over 95% of the time (3, 4).  Of course logs are not Cadillacs but the principle remains the same.  The potential for replenishment in log marshalling is considerable.

To Super Skid Or Not To Super Skid

In the regional model the maximum safety is not located in the forest with individual gangs but rather at the point of maximum consolidation – the super skid or log plant.  Two large-scale local supply chains in New Zealand use either a log making yard or a log making plant.  This is termed a super skid.  Another large-scale local supply chain carries out log making in conjunction with harvesting in the forest.  In Europe especially there seems to be a trend in mechanical harvesting to cut-to-length in the forest.  This might be alright for high value small scale operations but porting it to large-scale commodity plantation forests does not appear to be an optimal solution.

Local Performance Measures

Now that we have identified the constraint (leverage point) and proposed an exploitation strategy, how do we subordinate the subsystems in the process to the goal of the overall system?  We are really asking; how do we address deviations from our plan, our plan of attack (5).

Deviation from subordination results from;

(1)  Not doing what was supposed to be done for the non-constraints.

(2)  Doing what was not supposed to be done for the non-constraints.

Remember the customer is the constraint, so everything else in the system that is not the customer is a non-constraint.  Goldratt has two measures that cover the two cases above (5).  One is a measure of lateness which covers not doing what was supposed to be done, the other is a measure of waiting-in-process which covers doing what was not supposed to be done.

The measure of lateness is attached to any order at any node that can’t be filled immediately from stock for a buffer or can’t be filled by the due date for a cutting order.  If we can’t dispatch sufficient logs of a sufficient grade and size to fulfill an order to a downstream node, that order is late until dispatched in full.  We calculate its value as the throughput (sales value at the final point of sale less total variable costs) multiplied by the days late.  Let’s draw this.

The measure of waiting-in-process applies to any subsystem and is calculated at the raw material value multiplied by the total number of days resident in the subsystem for each stock unit.  If we do what we are not supposed to do (make logs that are not required) inventory tends to go up and this measure will increase quite quickly.  Let’s draw this as well.

If the subsystems are truly subordinated to the goal of the system, then lateness should trend towards zero (because we have buffered our system against reasonable variation) and waiting-in-process should be static or diminish (because we only do what we need to do to resupply the next node).  These measures are discussed more fully in the measurements section.  They are very important management tools.

Log Marshalling Is Not Simple Replenishment

If replace current forecasting with simple replenishment using buffers we would certainly avoid our current forecasting error.  Therefore, why can’t we just treat log marshalling as a simple replenishment system through a linear supply chain of dependent vendors or dependent nodes?  Well, it turns out that there are a number of reasons for this;

(1)  We must sell the entire tree, all of the time.

(2)  We must consolidate from numerous source nodes.

(3)  We must currently subordinate the source nodes to the point of sale.

(4)  We must position the protection in the place that best protects the whole system.

Of these; positioning the protection for the system in the place that does the most good is probably the most important; this mitigates our other major error – allocation error.  And the best place for the protection, unlike distribution, is closest to the customer.  In fact, however, in both cases the protection is a loaded at the point where volumes are most aggregated.  In marshalling that happens to be the port-side marshalling yards in the regional model or the super skid in the local model.  And if we are space constrained there, then we must move the protection to the next node up and ensure we have fast and frequent replenishment from that node.  Because marshalling deals with a convergent supply chain it is not a case of simple replenishment.

Let’s Think It Through

We begin to determine the replenishment buffers for line items at the point where we know the demand with the greatest degree of certainty – at the port-side log yards.  The log yards “see” the aggregate demand of the whole system, the peaks and the toughs smoothed out to the largest extent.  Then we work through the individual nodes in the next layer, and the next layer after that until we reach the point of origin.

We avoid forecasting error.

We avoid allocation error.

The unavoidable outcome is that we no longer have too little of the right material in the right place in the right time because our “plant” capacity (cutting gangs) is now “smoothed” and not wasted making unnecessary “emergency” jobs.

There are two caveats.  Firstly some nodes might be space constrained; then we have to consider moving the safety forward or failing that back to the next level up and improving transportation volume/frequency.  Secondly, because we must attempt to drive yield some “by-product (product in excess of market demand) may occur.  We have to accept in the first instance that market pricing will reflect this, but secondly we should seek mafia solutions for selling such product at a higher price than the market currently pays.  The counter argument is that some lines may always be in poor supply and hence yield very good prices – we won’t complain about this except maybe wish that 25 years earlier someone had got the figures correct!

What Are The Unavoidable Outcomes?

There are a number of unavoidable outcomes;

(1)  Increased throughput.

(2)  Decreased total inventory.

(3)  Stock-outs are no longer caused by the supply chain.

(4)  Over-stock is no longer caused by the supply chain.

(5)  Less emergency orders.

In fact we should have just the right amount of just the right material in just the right place – always.

But Wait, Reality Isn’t This Simple!

If this explanation seems quite simple and straightforward then that is excellent; then we know that we have developed an understanding of replenishment as applied to log marshalling.  If experience tells us that reality is more complicated than this generalized case, then that too is excellent.  Now we are in a position to better understand how to apply this methodology to our own particular situations.


Marshalling is like its inverse, distribution, without quite all of the complications of a divergent supply chain.  However in the particular case of log marshalling we must sell the whole tree and this adds an additional dimension to the problem.  Moreover marshalling, like distribution, is currently served by forecasting.  Thus trees are felled and logs are cut deterministically some time out from final sale.  The inevitable consequence is that sometimes we don’t make enough of the right logs and sometimes we make too many of the wrong logs.  When we make too many of the wrong logs some may reach their use-by date and have to be down-graded and re-transported as pulp logs.

We can overcome this by using the supply chain global solution of replenishment and buffer management.  In this case each node acts as a buffer for the next level and we can dispense with forecasting and it associated errors.  Moreover, we automatically place the system safety in the place where it provides maximal protection – the port-side marshalling yards or the super skid.  Furthermore, by increasing reorder and resupply frequency we can improve on current service delivery while decreasing total log inventory.


(1) Penfold, C., (2003) Log making to market or to order.  New Zealand Forest Industries, September, pp 20-22.

(2) Stein, R. E., (1994) The next phase of total quality management: TQM II and the focus on profitability.  Marcel Dekker, pg 36.

(3) Stern, G., (1995) GM expands its experiment to improve Cadillac's distribution, cut inefficiency, The Wall Street Journal (February 8th).

(4) Stern, G., and Blumenstein, R., (1996) GM expands plans to speed cars to buyers.  The Wall Street Journal (October 21).

(5) Goldratt, E. M., (1990) The haystack syndrome: sifting information out of the data ocean.  North River Press, pp 144-155.

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