to Implementing the Theory of Constraints (TOC)
Replenishment – Adding Value Through The Supply Chain
Replenishment is the method by which we add substantial value to the supply chain. We achieve this by increasing the throughput generated from the final customer – our constraint. In fact we must subordinate our whole supply chain to the constraint. That is we make sure that we have exactly the right stock, in the right place, whenever someone wants to buy it. Sound difficult? It’s not really. Not, if we think about what we are setting out to achieve.
Let’s step back for a moment to the manufacturing cases that we have examined and the step forward into supply chain. Previously, in section on production, we discussed increasing the throughput in make-to-order environments, an environment where timeliness is an explicit concern. We then examined make-to-stock and make-to-replenish environments, places where timeliness is less obvious but still a critical element. We represented our make-to-stock environment something like this;
The model consists of a manufacturing or production process where goods are transformed in some way and value is added. A make-to-stock production process is, however, decoupled somewhat from actual customer demand by a buffer of stock. Usually this indicates that the customers’ willingness to wait is less than our lead time to supply from pure make-to-order; so we must make product in advance and store it for essentially instantaneous availability. Timeliness doesn’t seem so critical, but it is. People want goods instantly.
Of course not all businesses contain a production or manufacturing process, that is, there may not be any transformation of goods carried out in the process. Instead, value is added by moving goods through both space (transportation) and time (storage) from a source of supply to the location of a demand. Rather than a flow from a process to a stock buffer as in manufacturing, we now have a flow from stock buffer to stock buffer. Let’s draw this.
It’s not too hard then to envisage a whole series of individual stock buffers feeding from one to another – nothing less than a supply chain. Let’s draw this.
Now we have four storage spaces, let’s call them nodes – it sounds far more impressive than shelf or pallet or regional warehouse – although each of these are indeed nodes on their own scale. Each node in the supply chain supplies the next node until the end-user, the final customer, makes a purchase. Also, each node in the supply chain is supplied by the previous node until the point of entry into the system, usually a production process in secondary industries or a raw material in primary/extractive industries. In effect we now have at each node, the storage space, these storage spaces are central to the replenishment solution; they are our stock buffers.
Each node could represent the quantity of a single type of stock unit in the chain as it passes from distributor to wholesaler to retailer for instance. Equally we could consider it to be the whole sum of all the different types of stock units at each of these stages. The diagram is generic; we can decide as the situation demands.
Clearly we could also have hybrid systems with both manufacturing and supply chain components, and the supply chain component might not be linear; but let’s leave these interesting facets until the distribution and marshalling pages. Replenishment is the motor for supply chain, it is the mechanism that ensures that we never miss a sale by not having the right material in the right place at the right time for the customer. Let’s have a closer look at the drivers of replenishment, but first, if you have a manufacturing background you might consider a small diversion.
Replenishment can be broadly described, or defined, as the frequent and rapid replacement of recent actual demand. The key is that there is no forecasting into the future, only the rapid response to the very recent past. We address timeliness in supply chain by the replenishment characteristics of our stock buffers. Through replenishment we can supply more goods in total, to the right place, at the right time, and most often with considerably less total stock in the system. The objective of course is to increase Throughput.
Each and every stock type at each and every node becomes its own replenishment buffer. The size and serviceability of the stock buffers is driven by how frequently and how rapidly we choose to replenish consumed stock and this is how we configure the solution. The configuration will depend upon the assumptions that we are willing to challenge about batching; both batching in time and batching of quantity.
Let’s draw this as a simple model.
Buffer management is how we monitor the motor’s performance. At a global level the stock buffers provide us with longer term feedback into the configuration when a certain degree of buffer violation or near violation becomes too common (or too uncommon) suggesting that a particular stock buffer needs to be resized to be fully effective and/or that the re-order/re-supply frequency or duration needs to be addressed.
Locally, the buffers, once in operation, signal replenishment quantities – our local prioritization system – they also absorb many small variations as well as providing day-to-day exception reporting indicating that there may be a potential stock supply violation – our local control.
Let’s add these local features to our model.
Buffer management is crucial; it filters important signals from the day-to-day noise of the system alerting us to potential problems before they become real problems, and it provides self-diagnosis that neither too much and nor too little protection is made available for each stock held.
Let’s now examine a little more about what we mean by replenishment.
Replenishment is one of those words, like for instance; quality or strategy, which means different things to different people depending upon their work environment and their experience. This is sufficient to cause quite a bit of confusion. In fact, it is probably fair to say that manufacturers have one view of replenishment and that supply chains have another view.
In general it seems that replenishment of a stock buffer is composed of two critical components;
Re-Ordering and Re-Supply
However, I will argue that there are at least 4 components and that the two additional ones should be insignificant, or at least rendered so. Nevertheless we should recognize their existence;
Re-Checking, Re-Ordering, Re-Supply, and Re-Stocking
Clearly, then, re-ordering and re-supply are the two aspects that may require the longest durations and hence impinge most upon our timeliness and the determination of the size of our buffers.
We need to examine re-ordering and re-supply separately before combining them together once again. In order to do this let’s exclude re-supply for the moment by something similar to that which applied mathematician do; “let us assume” re-supply is near instantaneous! If we do this then we can isolate some of the assumptions about re-ordering.
Let’s consider 2 re-ordering environments.
1. Fixed re-order quantity variable re-order frequency – batch lot manufacturing
2. Variable re-order quantity fixed re-order frequency – shipment lot supply chain
In fact there is a 3rd which we touched upon in manufacturing make-to-stock on the drum-buffer-rope page and which we will mention once again after considering the mechanism for determining buffer status. However, of the two above, the first, fixed-quantity, is very common in manufacturing, the world of batch lots. The second is fixed-frequency and is very common in supply chain, the world of shipment lots – and the subject of this page.
Clearly there is tremendous potential for people to say “I understand what you mean by replenishment,” when indeed the understanding is locked into the first case. I know, “I’ve been there, done that.”
So let’s work through both of these cases under the assumption of near-instant re-supply. Then we will have a look at the effect of non-instant re-supply, and how to accommodate this as part of replenishment.
If we think about it, all make-to-stock is replenishment of sorts. If we make 4000 new things that are standard items, and they don’t age, then it really doesn’t matter if we make a year’s supply and put them in the warehouse – although it would be better to be privately owned to embark on such a mission these days. Our only risk is that the demand might be for more than this plus our safety stock before we get around to producing these things again; therefore we might miss sales. If we sell less than 4000 in a year, then we just won’t make the remainder next year and bring our stock back up – right?
So in effect we did replenish the stock, it’s just that the cycle time is quite long. This is OK for companies with really deep pockets and really mundane things – industrial things. I’m not sure if we could find such a company still doing this today, well at least not on major items, but there certainly are stable established industrial firms who manufacture small volume items in their range once or twice a year. However, hopefully we are all in the business of making things to sell rather than making things to store.
What would happen then if we are supplying consumer goods or are making perishable items? Now if we make 4000 things it’s just quite possible that the market taste may change, or our competitors may bring out something ”new and improved” – even if it is only the label, or that some of the stock will pass it’s “use-by” date before it is sold. Now we risk not only missing sales if sales are greater than our forecast, we also have a very real risk of dead stock.
So what would happen if instead of making 4000 things once a year we made 1000 things once a quarter or heaven-forbid 333 things every month, or 83 and bit every week? Oops, round that up to 84 for safety – no make it 85. Can you see where we are heading? We are always replenishing whether with 4000 things or 85 things, but with increasingly smaller quantities and increasingly higher frequency. Moreover, because we are externally constrained and have processing capacity to spare, we can chase any localized market spurts, and we can also drop off quickly with any market downturns. In a word we have become responsive.
Let’s examine this graphically. Let’s retrieve our diagram for a hypothetical stock item from the finished goods section. A perfect saw-tooth diagram. In fact, it is too perfect for a processing environment. We all know that in any processing environment demand is never uniform; therefore the rates of drawdown are not uniform either. We need to take this into account. Let’s redraw it.
This looks more like reality. We replace goods with a new batch according to a signal triggered by the drawdown – the re-order point. When the demand rate is high or the replacement is slow we might drawdown on into our safety stocks a little. The frequency of replacement is driven by the rate of consumption. The replacement batch is of fixed-quantity. The maximum quantity is defined by the batch size policy that we chose.
We know from discussions of batch sizing throughout this site that reducing the process batch size will reduce the amount of inventory required to be held in finished goods and that there will be a equivalent increase in replacement frequency. Let’s draw this.
Our batch size in this example has been effectively halved and our replacement frequency has doubled as a consequence. We are replenishing with smaller quantities more often and maintaining the service level with half of the previous inventory. Let’s go to the next step and halve process batch size once more.
Now the batch size of each replacement is one quarter of the original batch size and the replacement frequency is 4 times the original. Clearly we are moving towards replenishment, less and less stock needs to be held on hand and it is replaced more and more frequently.
Effectively as batch size decreases the maximum stock in absolute terms approaches closer and closer to the re-order point stock level. Of course the ideal batch size would be a unit of 1 – single piece transfer/just-in-time.
Therefore replacement in a processing environment is characterized by;
Fixed Re-Order Quantity & Variable Re-Order Frequency
The process requires a finished goods stock – but it should be as small as possible to decouple the customer from the process. The customer can still get stock immediately, and the process replaces it with as small a batch as possible.
In re-order point systems the batching policy is explicit. However, in some min-max systems the batching policy may be more implicit; “the batching policy is hidden under the size of the max minus the min (1).” The batch size is defined by the physical and especially the policy constraints of the process.
Most manufacturers will recognize the above discussion as what they term replenishment – and this type of fixed-batch replenishment does find its way into supply chain too. However, it generates two traps that we should be aware of and try to avoid.
The first trap comes from synchronization. Consider the following.
Within any two layers of a supply chain there may be a one-to-many relationship. In a min/max system we need to consider what happens when more than one downstream node hits the re-order point at about the same time? The upstream node experiences a “wave” of demand. Improbable? Not at all.
How could such a synchronization arise in the first place, from simple chance? Or did it arise from the last synchronous re-supply from the upstream node? This seems far more probable. Often times we create our own problems for ourselves. This type of problem creates large waves in upstream nodes when gentle and continuous downstream consumption is the reality.
The second trap comes from the number of layers or levels of nodes in the supply chain.
In a min/max system it can take quite some time for a signal for replenishment to move back from a single downstream node to an upstream node. If we multiply this effect by several layers or levels then the delay soon becomes quite considerable (2).
Combine these two situations, a significant number of layers and some one-to-many relationships and then even a simple linear supply chain is suddenly not so simple any longer.
Let’s look at the alternative then; fixed re-order frequency with near-instant re-supply; a more common replenishment system found in supply chains.
In supply chain, as opposed to manufacturing, it is possible to break out of fix-quantity re-orders and instead fix the re-supply frequency. So we need to investigate this as well.
Let’s start again at the beginning once again with our perfect saw-tooth graph.
In this instance we no longer have a re-order point based upon quantity. Instead we have a re-order date. Re-ordering is done at some regular interval; once a month at the end of the month, once a week at the end of the week, once a day at the end of the day. In the absence of manufacturing batching constraints the imperfection that we are seeking in this graph and which we would indeed see in the real world is that the replacement amount will be variable rather than constant as we have drawn. Let’s have a look at that.
Total inventory oscillates below some full value that we have determined as necessary to protect customer demand. The re-order dates occur at fixed-frequency; once a day or once a week or once a month or some similar measure. The difference between the full value and the current value at the re-order date determines the amount of the re-order.
Just as in the processing environment where we could reduce the quantity in each batch, here we can increase the frequency and obtain the same effect; reduced inventory of finished goods stock without degrading service levels. Let’s halve the re-order date duration and thereby double the re-order frequency. This is the equivalent of ordered once a fortnight instead of once a month, or ordering once a week instead of once a fortnight.