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Production Release Control: Paced, WIP-Basedor Demand-Driven? Revisiting the Push/Pulland Make-to-Order/Make-to-Stock Distinctions

George Liberopoulos

Abstract We consider three elementary mechanisms for controlling the release ofparts for production in manufacturing systems: 1) setting an external productionpace by controlling the raw part arrival process, 2) authorizing releases based on thework-in-process (WIP) level in all or parts of the system, and 3) releasing new partsin response to actual demands. We present and compare different variants of thesemechanisms on a simple serial flow line, using a queuing network representation,and we use these variants as a basis to revisit the push/pull and make-to-order/make-to-stock distinctions. We extend our descriptions and definitions to include advancedemand information and forecasts.

1 Introduction

The last two decades have seen a surge in the literature related to pull control,kanban-type control, WIP control, and more generally token-based production con-trol systems. Not only have many generalizations, extensions, and variants of theoriginal kanban system been introduced, analyzed, and compared (e.g., generalizedkanban control system (GKCS), CONstant WIP (CONWIP), production authoriza-tion card (PAC), paired-cell overlapping loops of cards with authorization (POLCA),extended kanban control system (EKCS), customized token-based system (CTBS),heijunka kanban, among others), but several reviews (e.g., [30], [16], [31], [13],[17]) and new approaches for representing and analyzing these systems (e.g., [7],[9], [12], [2], [1]) have appeared in the literature in the last five years only. New de-velopments have also taken place within the last five years in extending the analysisand performance of pull systems to include features such as advance demand in-formation (e.g., [47], [33], [38], [8], [22]), lot sizing (e.g., [45]), multiple products

George LiberopoulosDepartment of Mechanical Engineering, University of Thessaly, Volos, Greece e-mail:[email protected]

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2 George Liberopoulos

(e.g., [44], [19], [29]), parameter and system optimization (e.g., [18], [41]), con-trol point optimization (e.g., [3], [48]), and new simulation-based studies have beenpublished (e.g., [15], [28], [43], [40], [26]).

Despite this intensive activity in the literature, or perhaps because of it, the defi-nition of certain important concepts remains unclear after all these years. Differentauthors still use the same name to describe different production release control con-cepts or different names to describe the same concept. This would not be a problemif the description of the concept were absolutely clear. Often, however, this is not thecase, because many descriptions involve imprecise statements, such as “productionrelease is based on system status” (what is system status? WIP, pending orders?) or“production release is done in advance of demand” (when is the demand timed? atthe arrival time of a customer order, at the due-date of a customer order?).

An important concept that is still a source of confusion is the distinction betweenpush and pull and its relationship to the distinction between make-to-order (MTO)and make-to-stock (MTS). After following the related literature for over the last twodecades, it appears that there is still no generally agreed upon definition of the maindistinction between push and pull. Not only have there been several definitions ofthe push/pull distinction, but some researchers and practitioners seem to have shiftedtheir perception of this distinction over time. There have also been several reviewand overview papers that discuss the push/pull distinction; most of these papers endup adopting one or the other definition. The same holds for many textbooks.

For example, in Chapter 7 of his book Production and Operations Analysis, Nah-mias [42] adopts the more traditional view that a pull system is one in which itemsare moved from one level to the next only when requested, while a push system isone in which production planning is done in advance. Under this view, he statesthat MRP is the basic push system and kanban is the earliest of the pull systems. Tofurther clarify the push/pull distinction, he cites the definition in [24], according towhich a pull system initiates production as a reaction to present demand, while apush system initiates production in anticipation of future demand. Thus, he writes,MRP incorporates forecasts of future demands, while JIT (the philosophy that growout of the kanban system) does not.

In Chapter 13 of their book Manufacturing Planning and Control for SupplyChain Management, Vollman et al. [46] state that the key distinction between pushand pull pertains to whether the individual work centers are allowed to utilize ca-pacity (keep “busy”) without being driven by a specific end-item schedule (push) orare authorized to produce only when it has been signaled that there is a need formore parts in a downstream department (pull) .

A somewhat related view is taken by Zipkin [49] in Chapter 8 of his book Foun-dations of Inventory Management, where he writes that in a pull system, customerdemands trigger all other events in the system, directly or indirectly, as the demandinformation propagates backwards from the end to the beginning of the system. Un-der this definition, he states that both kanban and base-stock are pull systems.

In a paper that presents a unified framework for modeling and comparing pullsystems, Liberopoulos and Dallery [34] side with the view of Zipkin that in a pullsystem, production is triggered by actual demands for finished products, which im-

Production Release Control 3

plies that in a push system, production is initiated independently of demands. Inthe follow-up paper, Liberopoulos and Dallery [36] chronicle the “debate” on thepush/pull distinction and extend their framework to include lot sizing. This frame-work is further extended in [39] to include advance demand information.

In Chapter 10 of their book, Factory Physics, Hopp and Spearman [20] takea seemingly different view and state that a pull system authorizes the release ofwork based on system status, while a push system schedules the release of workbased on demand. They further specify that a pull system only allows the releaseof work when a signal that is generated by a change of system status (typically,the completion of work at some point in the system) calls for it. Another usefulway to think about this distinction, they argue, is that push systems are inherentlyMTO, while pull systems are MTS and that, viewed this way, the base-stock is apull system, whereas MRP is a push system. They maintain that the key benefitsof a pull system arise when it establishes a WIP cap, i.e., a limit on the maximumamount of inventory in the system. Hence, a fundamental distinction between pushand pull, they conclude, is that pull systems control WIP and observe throughput,while push systems control throughput and observe WIP. They also describe pullsystems as being inherently “rate-driven,” in that we fix the level of WIP in themand let them run. These statements suggest that pull systems are not necessarilydriven by demands, although at some later point, the authors write that CONWIPand kanban are both pull systems in the sense that releases into the line are triggeredby external demands.

In [21], Hopp and Spearman argue that practitioners initially equated pull withkanban and MTS, and push with MRP and MTO, at a strategic level, but after the1990’s, these associations were completely reversed and pull became associatedwith MTO, whereas push with MTS, at a tactical level, causing confusion amongpractitioners. They then proceed to define a pull system as one that explicitly limitsthe amount of WIP that can be in the system, while a push system as one that hasno explicit limit on the amount of WIP that can be in the system. They also revisetheir earlier view that push systems are inherently MTO and pull systems are MTSby stating that the MTO/MTS distinction is orthogonal to the push/pull distinction.They argue that, under this view, kanban, CONWIP, (K,S), POLCA, PAC, and MRPwith a WIP constraint are pull systems, whereas MRP, base-stock, and installation-stock (Q,R) are push systems.

In a recent book, Engineering Production Control Strategies, Karrer [25] adoptsthe definition of Hopp and Spearman. Other authors (e.g., [17]) also adopt this defi-nition.

Based on the discussion above, we can group the definitions of the push/pulldistinction into the following three general definitions:

Definition 1. A pull system initiates production as a reaction to present demand,while a push system initiates production in anticipation of future demand.

Definition 2. In a pull system, production is triggered by actual demands for fin-ished products, while in a push system, production is initiated independently ofdemands.


Definition 3. A pull system is one that explicitly limits the amount of WIP that canbe in the system, while a push system has no explicit limit on the amount of WIPthat can be in the system.

In this chapter, we adopt Definition 2, but we also discuss the other two defini-tions.

If the push/pull distinction is still under question, the MTO/MTS distinctionshould be easier to agree on. The MTO/MTS distinction has to do with whether fin-ished goods are produced to be stocked or to fill specific customer orders (demands).Liberopoulos and Dallery [36] define a MTS system as one in which parts are pro-duced up to a certain target inventory level before the actual demands for them havearrived. When a demand arrives to the system, it is satisfied from the stock of fin-ished goods, if such stock is available, and it triggers a production release order fora new part to replenish the finished goods inventory. In a MTO system, no inven-tory is produced ahead of time; instead production is initiated to satisfy a particularorder whenever such an order arrives to the system. Therefore, production followsdemand. Somewhere in between MTO and MTS, but perhaps closer to the latter, liesthe notion of make-to-forecast (MTF). In a MTF system, parts are produced aheadof time to meet forecasted demands, before the actual demands for finished partshave arrived. We summarize these descriptions into the following definition:

Definition 4. In a MTO system, production releases are initiated to meet actual cus-tomer orders (demand), while in a MTF system, production releases are initiatedto meet forecasts of customer orders. In a MTS, production releases are initiatedto replenish the finished goods inventory and bring it up to a specified target level.Therefore, in MTO systems, production follows demand, while in MTF and MTSsystems, production precedes demand, where the demand is timed at the due dateand not the arrival time of a customer order.

One of the issues that we will address in this paper is the relationship betweenthe push/pull distinction and the MTO/MTS distinction.

As we wrote earlier, Hopp and Spearman [21] maintain that Definition 3 ofthe push/pull distinction, which they propose, is orthogonal to the definition ofMTO/MTS. Therefore, they argue that both push and pull systems can be eitherMTS or MTO and illustrate this with some examples.

We agree that the push/pull distinction should be separate from the MTO/MTSdistinction. However, as we adopt Definition 2 for the push/pull distinction, we ar-gue that the MTO/MTS distinction, which has to do with the timing of productionreleases relatively to the timing of demands, only makes sense in pull systems, be-cause in push systems, production is initiated independently of demands. Moreover,in Section 4.2, we argue that MTF systems can be either pull or push, depending onwhether forecasts are based on actual demands or are generated independently ofthe demands.

Finally, Definition 1 of the push/pull distinction seems to equate push with MTOand pull with MTF and MTS.

Our goal in this chapter is to try to sort out the above concepts. To this end,we will present different production control systems and describe exactly how each


system works using a queuing network representation. In the end, we will put labelson these systems (e.g., push, pull, etc), but we need to point out that our intention isnot to convince that these labels are written in stone. Our intention is to clarify andclassify different ways of production release control and see how these ways relateto each other and can be combined with each other. Ultimately, we want to be ableto make statements such as, “Here is a particular production release control systemand this is exactly how it works. We happen to call it ‘X’ (e.g., ‘a push system witha WIP cap’). Others, call it Y. This is fine, as long as we understand that we aretalking about the same thing.”

The remainder of this chapter is organized as follows. In Section 2, we presentseveral production control systems in the absence of demands, First, we considera basic system with controlled raw part arrivals but without any WIP control, andthen we turn our attention to systems with WIP control. In Section 3, we revisitsome of these systems in the presence of demands and use them as a basis to discussthe push/pull and MTO/MTS distinctions. In Section 4, we extend our descriptionsand definitions to include advance demand information and forecasts. Finally, weconclude in Section 5.

2 Production Control in the Absence of Demands

In this section, we present several production control systems without accounting forthe demands for finished goods. Initially, we look at a system without WIP controlbut with controlled raw part arrivals. Then we turn our attention to systems in whichWIP is controlled in various ways.

In practice, no production system operates in the absence of demands. Even whendemand is excessive, as is often the case in the initial phase of a popular gadget’slifecycle, production is still driven by orders, which are unavoidably backordered.Therefore, the systems that we present in this section are not encountered in reallife. We consider them, because they are key components of more complex systemsthat take into account the demand for finished goods. They can be also viewed asdemand-saturated systems, i.e., systems with infinite demands, that can be used fordesign purposes to estimate the maximum throughput of the physical productionsystem that they are applied to. The maximum throughput is important to knowbecause it sets the maximum demand rate that the system can meet in the long run.

2.1 System without WIP Control

Figure 1 shows a basic production system which is a simple flow line consisting offour workstations in series, separated by buffers of infinite capacity. The worksta-tions are represented by ovals and are denoted by WSi, i = 1, · · · ,4. The buffers arerepresented by open square boxes and are denoted by Pi, i = 1, · · · ,3. Each worksta-


Fig. 1 Production system with infinite-capacity buffers

tion consists of a machine, represented by a circle, with an input buffer of infinitecapacity in front of it, represented by a small open square box. Upstream of WS1there is a raw parts buffer, denoted by P0, which receives raw parts that arrive ac-cording to a controlled raw part arrival process, denoted by RP. RP can be thoughtof as the machine of a pseudo-workstation that is supplied by an infinite source ofparts. Downstream of WS4 there is a finished goods buffer, denoted by P4.

We assume that there is no control of the machine processing rates, so whena machine works on a part, it processes it at full speed. Thus, when the machineof workstation WSi, i = 1, · · · ,4, finishes processing a part, it pushes it to the inputbuffer of the next downstream workstation, WSi+1 (or to the exit of the system, if i=4). The machine then pulls a new part from its input buffer and starts processing itas fast as it can. If no part is available in its input buffer, then the machine is starved.The part that is pushed downstream passes through buffer Pi but does actually spendany time in Pi; hence Pi is always empty. The same holds for buffer P0 which is fedby the pseudo-machine RP. As buffers P0 to P4 are always empty, they are drawnwith dotted lines to indicate that they could have been omitted from the picturewithout changing anything in the system behavior.

We should mention that in some real-life manufacturing systems, it has been ob-served that the workload affects the performance of the system, e.g., the processingrates of the machines. One of the reasons for this is that the workers who operate themachines tend to work more efficiently when the workload is at some ideal value.If the workload is below this value, they are not pressured enough, and if it is aboveit, they are over pressured. This is an interesting issue, but it is outside the scope tothis chapter. We refer the interested reader to [4].

Unlike the production rates of the machines in workstations WSi, i = 1, · · · ,4, the“production rate” of pseudo-workstation RP (raw part arrival rate) is controlled andessentially sets the production pace for the rest of the system. In fact, it is the onlycontrol parameter in the system.

Normally, the “production rate” of RP (raw part arrival rate) should be set to avalue which is lower that the production rates of the actual workstations. In this case,RP acts as the bottleneck (slowest) workstation, and all the buffers downstream ofRP have a finite number of parts; hence, the WIP is finite. Moreover, the throughput(output rate) of the system is equal to the production rate of RP. Such a system wouldbe characterized as push, based on Definition 3, because throughput is controlledand WIP is observed.

If the production rate of RP is set higher than the production rate of WS1, theneventually the input buffer in front of WS1 will be flooded with infinite raw partsand therefore WS1 will always busy. In fact, if all the workstations (including RP)upstream of any given workstation WSi are faster than WSi, then the input buffer


Fig. 2 Flow line with finite-capacity buffers

of WSi will eventually be flooded with infinite parts, even if it is not the bottleneckworkstation. The only way that this will not happen is if the bottleneck workstationis RP.

Many readers will recognize in Figure 1 an open network of tandem queueswhere RP is the arrival process of jobs to the rest of the system.

2.2 Systems with WIP Control

In this section we present several known WIP control mechanisms applied to thebasic production system considered in Section 2.1. To motivate the discussion, firstwe consider a classical flow line model with finite buffers and then move on todescribe the WIP control mechanisms.

2.2.1 Flow Line with Finite-Capacity Buffers

Figure 2 shows a basic production system consisting of four workstations separatedby finite-capacity buffers.

The system is identical to the basic production system shown in Figure 1, exceptthat there are no input buffers in front of the machines, and buffers Pi, i = 1, · · · ,4have finite capacities. To indicate this, they are shown as closed square boxes withpartitions. The total capacity of WSi plus Pi is denoted by Ki. Assuming that themachine in WSi can hold only one part, this means that the capacity of buffer Pi isKi −1.

In addition to the control of raw part arrivals through process RP, the systemalso has WIP control. Namely, the number of parts in WSi plus Pi is not allowedto exceed the WIP limit Ki, i = 1, · · · ,4. More specifically, when workstation WSi,i = 1, · · · ,4, finishes processing a part, it pushes it downstream, but it does not im-mediately pull a new part from its upstream buffer Pi−1, unless there is availablespace in Pi to store that part when it is finished; If there is no available space in Pi,then the machine in WSi is blocked from pulling a new part. Of course, if no partis available in buffer Pi−1, then the machine is starved. Note that a machine may beblocked and starved at the same time. Machine in WS4 is never blocked, becausewhen a part finishes its processing at WS4, it immediately leaves the system. Beforeleaving, the finished part instantaneously passes though the finished goods buffer P4but does not spend any time in it; therefore, buffer P4 is always empty. As previ-


Fig. 3 Flow line with finite-capacity buffers and BBS-PNO, represented with the use of PA cards

ously, it is drawn with dotted lines to indicate that it could have been omitted fromthe picture without changing anything in the system behavior.

If the “production rate” of RP (raw part arrival rate) is set higher than the produc-tion rate of WS1, then the raw parts buffer P0 will eventually be flooded with infiniteraw parts and consequently WS1 will never be starved. In this case, the system willhave given up the raw part arrival control, but the WIP control will still be there toensure that no buffer (except P0) grows to infinity. Of course in real life, no buffercan accommodate infinite parts. In practice, however, it is not unusual that the rawparts supply department of a firm would try to ensure that P0 almost never runs outof raw parts.

The system described above is a specific variant of a manufacturing flow linewith finite buffers. The study of such lines has been a particularly active area ofresearch for over 30 years. Much of the literature on flow lines has focused onthroughput analysis. For a recent review, see [32]. The blocking mechanism thatwe described above is only one of several possible blocking mechanisms and isreferred to as blocking before service with position non-occupied (BBS-PNO). Moreblocking mechanisms and flow line variations are described in [10].

The representation of the system shown in Figure 2 is not sufficient for describ-ing the blocking mechanism. An alternative, precise representation of the systemdescribed above is shown in Figure 3, where the WIP control on each workstationand its downstream buffer is implemented with the use of production authorization(PA) cards.

In this representation, buffers PAi, i = 1, · · · ,4, have infinite capacity, as dobuffers Pi in the system in Figure 1. In order for the machine in WSi to pull a newpart from buffer PAi−1 (buffer P0, in the case of WS1) and start working on it, a freePA card must be available in buffer Ai. If such a card is available, then it is attachedonto a part in buffer PAi−1 and together they are released into WSi; therefore, buffersAi an PAi−1 are linked together in a synchronization station. When the part finishesits processing in WSi, it is pushed into buffer PAi, with the card still attached to it.The card is freed from the part when the part leaves PAi to enter the next down-stream workstation WSi+1. The free card is returned to buffer Ai. The intermediarybuffers are denoted by PAi instead of by Pi to indicate that they contain parts (“P”)with production authorization cards (“A”) attached to them. Essentially, the cards inbuffer Ai represent the number of free positions in finite buffer Pi in Figure 2. Notethat as PA4 is always empty, buffer A4 will always have either K4 or K4 − 1 free


Fig. 4 Production system with kanban control at the workstation level

cards, given that WS4 can only hold zero or one part; therefore the behavior of thesystem for any value of K4 > 1 is identical to its behavior when K4 = 1.

2.2.2 System with Kanban Control at the Workstation Level

The system in Figure 4 is identical to the system in Figure 3, except that each ma-chine has an input buffer of infinite capacity in front of it as was the case in thesystem in Figure 1.

Alternatively, the system in Figure 4 is identical to the system in Figure 1, exceptthat WIP is controlled at the individual workstation level. More specifically, thenumber of parts in each workstation WSi plus its downstream buffer PAi is notallowed to exceed the WIP limit Ki; therefore, Ki is a WIP cap, i = 1, · · · ,4. Inthe case of the last workstation, PA4 is always empty (hence it is drawn in dottedlines). This implies that if each finished part released its PA card immediately afterexiting WS4, instead of after exiting P4, nothing would change in the behavior ofthe system.

If the “production rate” of RP (raw part arrival rate) is set higher than the pro-duction rate of WS1, then the raw parts buffer P0 will eventually be flooded withinfinite raw parts. In this case, as soon as free PA of WS1 is returned from bufferPA1 to buffer A1, it will immediately be attached onto a raw part in P0, authoriz-ing its release into WS1. This means that buffer A1 will always be empty, and thenumber of parts in WS1 plus PA1 will always be equal to K1; K1 will therefore be aWIP constant rather than a WIP cap. When P0 has infinite raw parts, the productionsystem attains its maximum throughput. Such a system would be characterized aspull, based on Definition 3, because WIP is controlled and throughput is observed.

If the production rate of RP is lower than the maximum throughput of the system,then the system will be able to absorb all the raw parts generated by RP, and thethroughput (output rate) of the system will be equal to the production rate of RP(arrival rate of raw parts). Such a system might be characterized as hybrid push/pull,based on 3, because throughput is controlled and WIP is limited.

Many readers will recognize in Figure 4 the classical single-card kanban system,where the kanbans (PA cards) are defined at the individual workstation level. For thisreason, we used the name kanban control at the workstation-level to describe thesystem. We caution, however, that the system in Figure 4 is not a complete kanbansystem, because it is not driven by demands. Its behavior, however, is identical to the


Fig. 5 Production system with CONWIP control

Fig. 6 Production system with CONWIP control (equivalent representation)

behavior of a saturated kanban system, i.e., a kanban system with infinite demandsfor finished goods.

2.2.3 System with CONWIP Control

Controlling WIP at individual workstations (Figure 4) is at one extreme of the dif-ferent ways of controlling WIP. Figure 5 shows the other extreme case where WIPis controlled at the level of the entire system.

More specifically, in the system in Figure 5, the number of parts in the entiresystem is not allowed to exceed the WIP limit K1−4; hence, K1−4 is the WIP cap ofthe entire system. As in the system in Figure 4, buffer PA4 is always empty (hence itis drawn in dotted lines). This implies that if each finished part released its PA cardimmediately after exiting WS4, instead of after exiting the finished goods buffer,as is shown in Figure 6, nothing would change in the behavior of the system. Inaddition, all the other buffers PA1–PA3 are also empty.

If the “production rate” of RP (raw part arrival rate) is set higher than the produc-tion rate of WS1, then the raw parts buffer P0 will eventually be flooded with infiniteraw parts. In this case, as soon as free PA card is returned from buffer PA4 to bufferA1, it will immediately be attached onto a raw part in P0, authorizing its release intoWS1. This means that buffer A1 will always be empty, and the number of parts inthe entire system will always be equal to K1−4; K1−4 will therefore be a WIP con-stant rather than a WIP cap and the resulting system will be a CONWIP (CONstantWIP) system [20]. Based on Definition 3, this is a pure pull system, because WIP iscontrolled and throughput is observed.

Note that the PA mechanism in Figure 5 is identical to the kanban mechanismin Figure 4, except that the PA cards (kanbans) are not defined at the individualworkstation level but at the level of the entire system. For this reason, some authors(e.g., [34]) view the CONWIP system in Figure 5 as a single-stage kanban system,


Fig. 7 Production system with multi-stage sequential kanban control

Fig. 8 Production system with multi-stage sequential kanban control (equivalent representation)

because all the workstations have been grouped into a single stage and the WIP ofthat stage is controlled with a kanban-type mechanism.

2.2.4 System with Multi-Stage Sequential Kanban Control

As Hopp and Spearman [20] note, the systems in Figures 4 and 5 are at the extremesin a continuum of CONWIP-based configurations. Figure 7 shows an intermediatecase where the system is divided into two stages and a WIP control loop is imposedon each stage. Hopp and Spearman [20] call such a system multi-loop CONWIP.Other authors (e.g., [34]) use the name multi-stage (sequential) kanban to describeit, because the WIP control is implemented by a kanban-type mechanism. Irrespec-tively of the name, the operation of the system is the same. More specifically, thenumber of parts in workstations WS1 and WS2 plus their downstream buffers PA1and PA2 is not allowed to exceed the WIP cap K1−2. A similar WIP cap, K3−4, is setfor workstations WS3 and WS4 plus their downstream buffers PA3 and PA4.

As in the other two systems in Figures 4 and 5, the finished goods buffer PA4is always empty. This implies again that if each finished part released its PA cardimmediately after exiting WS4, instead of after exiting the finished goods buffer, asis shown in Figure 8, nothing would change in the behavior of the system.

Also, as in the previous two WIP controlled systems, if the “production rate” ofRP (raw part arrival rate) is set higher than the production rate of WS1, then the rawparts buffer P0 will eventually be flooded with infinite raw parts. In this case, bufferA1 will always be empty, and the number of parts in WS1, PA1, WS2, and PA2 willalways be equal to K1−2; K1−2 will therefore be a WIP constant rather than a WIPcap. Based on Definition 3, such a system will be a pure pull system.


Fig. 9 Production system with echelon kanban control

2.2.5 System with Echelon Kanban Control

The most comprehensive way of controlling WIP is to control the WIP betweenthe entrance of any two workstations. In our 4-workstation example, this would beequivalent to setting up WIP control loops in workstations 1, 1–2, 1–3, 1–4, 2, 2–3,2–4, 3, 3–4, and 4. This would imply that some of the WIP control loops are over-lapping; as a result, a part would be carrying several PA cards from different WIPcontrol loops as it moved downstream the production process. Such configurationshave been studied in [14], [16], and [17], under the name controlled token-basedsystems (CTBS).

Although it is possible that some combinations of overlapping WIP control loopsmight perform better than others, we think that using too many overlapping WIPcontrol loops would be too confusing and difficult to handle for practical purposes.We find, that among all the possible combinations of overlapping WIP control loops,the case of nested kanban loops, such as the one shown in Figure 8, may be ofinterest for the following reason.

In general, CONWIP (or single-stage kanban) control allows more flexibility inthe production system than any other WIP control mechanism, because it controlsthe release of parts at the entrance of the system only and nowhere else. CONWIPis also very simple to implement. A potential shortcoming of CONWIP is that whena part is released into the system, it is pushed through without any further control.This may be fine in many situations, but it may be problematic in other situations.What would happen, for example, if the parts in the last two workstations, WS3and WS4, required special storage conditions, making the inventory holding costmuch higher in them than in the first two workstations, WS1 and WS2? What ifadditionally, WS3 and WS4 were relatively slow? In this case, the CONWIP systemwould indiscriminately push parts to workstations WS3 and WS4. These parts wouldthen accumulate in front of the slow workstations WS3 and WS4, incurring highinventory cost.

The first step towards dealing with such a situation is to recognize that the lasttwo workstations should be treated differently than the first two workstations. Onway to signal this is to set up two sequential WIP control loops (sequential multi-stage kanban or multi-loop CONWIP), as in Figure 7, and set K3−4 to a small valueto limit the WIP in the last two workstations. The sequential multi-stage kanbansystem in Figure 7, however, is a local control scheme, because the decision of au-thorizing the release of parts in the first two workstations is based on the WIP in


these workstations and does not take into account the WIP in the last two work-stations. The nested kanban mechanism in Figure 9, on the other hand, is a globalcontrol scheme, because the decision of authorizing the release of parts at any con-trol point in the system (including the first two workstations) is based on the WIP inthe entire system downstream of the control point. Due to its global nature, Buza-cott and Shanthikumar [6] call the demand-driven version of the system in Figure 9,integral control system.

In a way, the movement of PA cards in the nested system of Figure 9 is similar tothe movement of demands in an echelon stock (Q,R) policy, whereas the movementof PA cards in the sequential system of Figure 7 is similar to the movement ofdemands in an installation stock (Q,R) policy; for this reason, the demand-drivenversion of the system in Figure 9 is referred to as echelon kanban control system in[36] and [27]. We adopt the same name here. Gonzalez-R et al. [17], on the otherhand, call it token-based base-stock system, most likely because the movement ofPA cards in the nested system of Figure 9 resembles the movement of demands in abase-stock system, which we will examine in Section 3.1.

3 Production Control in the Presence of Demands

In this section, we revisit some of the production control systems that we presentedin Section 2, only this time in the presence of demands for finished goods. We dis-tinguish between two cases. In the first case, the demands for finished goods do notgenerate any further demands upstream of the finished goods buffer. In the secondcase, the demands for finished goods generate further demands for semi-finishedgoods and raw parts that are transferred upstream the system. We will use these twodistinct cases to characterize the systems as push or pull, based on Definition 2. Wewill further characterize the pull systems as either MTO or MTS.

3.1 System without WIP Control in the Presence of Demands

We revisit the system without WIP control that we presented in Section 2.1, onlythis time in the presence of demands for finished goods.

3.1.1 System with Demands for Finished Goods Only

Figure 10 shows a system which is identical to the basic production system depictedin Figure 1, except that the finished goods coming out of WS4 do not immediatelyleave the system by instantaneously passing through the finished goods buffer P4;instead, they are stored in P4, waiting to be matched to customer demands for fin-


Fig. 10 Production system with infinite-capacity buffers and demands for finished goods

ished goods that arrive to buffer D5. This means that P4 is not always empty, as wasthe case in Figure 10; for this reason it is not drawn in dotted lines.

Buffers P4 and D5 are linked in a synchronization station. If a part is available inP4, but no customer demand is available in buffer D5, then the part waits in P4 untilsuch a demand arrives to D5. Similarly, if a demand is available in D5 but no partsare in P4, then the demand waits in D5 until a part enters P4 from WS4. If a part isavailable in P4 and a demand for such a part is available in buffer D5, then the partis immediately delivered to the customer that placed that demand and the demandis satisfied; hence, it is dropped from D5. This means that at least one of the twobuffers, P4 and D5, is empty at all times.

Note that the incoming customer demands are for finished goods only and donot generate any further demands for semi-finished goods or raw parts. Therefore,the entire system upstream of P4 is not informed of the demands and behaves ex-actly like the system in Figure 1, i.e., it produces parts with no control other thanthat stemming from the controlled raw part arrival process RP. This type of con-trol, however, is completely exogenous or open loop, because it does not take intoaccount either the state of the system (WIP) or the external disturbance that is sup-posed to drive the system (demand). In Section 3.1.3, we claim that the latter is acharacteristic of a push system.

Normally, the “production rate” of RP (arrival rate of raw parts) should be setequal to the average demand rate, so that eventually all the finished parts will bematched to demands and vice versa. In this case, the number of parts in buffer P4and the number of demands in D5 will be finite.

If the demand arrival process were fairly invariable and the production worksta-tions well balanced (i.e., with more or less equal production rates), then the pacedproduction system in Figure 10 might perform reasonably well, as it would result ina relatively smooth material flow.

3.1.2 System with Demands for Finished Goods that also Generate Demandsfor Semi-Finished Goods and Raw Parts

Figure 11 shows a system which is identical to the system in Figure 10, except thateach incoming customer demand for finished goods also generates a demand for a


Fig. 11 Production system with infinite-capacity buffers and demands for finished goods, semi-finished goods, and raw parts (base-stock system)

semi-finished part in buffer P2 and a demand for a raw part stored in P0; these twodemands enter buffers D3 and D1, respectively. In this case, in order for a raw partto enter WS1, not only must such a part exist in P0, but a demand for it must alsoexist in buffer D1. Similarly, in order for a semi-finished part in P2 to be released inWS3, a demand for it must be available in buffer D3.

Although the release of raw parts into the system is still controlled by the exoge-nous raw part arrival process RP, the release of parts at various other control pointsof the system (including the entrance of the system), is also driven by demands.These control points are the entrance of WS1, the entrance of WS3 and the exit ofP4. Of course, there could be other control points (e.g., the entrance of WS2 and theentrance of WS4), but we omit them for space considerations.

The maximum throughput of the demand-responsive production system in Figure11 is equal to the throughput of the demand-ignoring system in Figure 1. If themaximum throughput is smaller than the demand rate, then the demand-responsivesystem in Figure 11 will not be able to meet the demands, and the demand buffersD1, D3, and D5, will eventually grow to infinity. In this case, the demand-responsivesystem in Figure 11 will behave exactly as the demand-ignoring system in Figure 1.

If the maximum throughput of the system in Figure 11 is smaller than the demandrate, however, then the system will be able to meet all the demands, and its through-put will be exactly equal to the demand rate. Moreover, the WIP in the system willbe finite. This latter case is more realistic and is of interest.

If the “production rate” of RP (raw part arrival rate) is set higher than the produc-tion rate of WS1, then the raw parts buffer P0 will eventually be flooded with infiniteraw parts. In this case, the system will have given up the raw part arrival control, butthe demand response will still be there. A potential disadvantage of having infiniteraw parts (or practically, a very large number of raw parts) is that if a very largenumber of customer demands arrive in a short period of time, then an equal (verylarge) number of raw parts will enter WS1, unnecessarily burdening the WIP.

Normally, however, the processing rate of RP (raw part arrival rate) should be setequal to the demand rate, which implies that buffer P0 will not grow to infinity. Inthis case, the raw part arrival process plays an important control role as it sets a limiton the release pace. Thus, if a very large number of customer demands arrive in a


short period of time, the controlled raw part arrival process RP prohibits the releaseof an equal (very large) number of raw parts into WS1, which may unnecessarilyburden the WIP.

The initial state of the controlled buffers P2 and P4 is defined as the numberof parts in these buffers before any customer demands have arrived to the system.Contrary to all the systems presented earlier, the initial states of buffers P2 and P4 inthe system in Figure 11, denoted by S2 and S4, respectively, play an important role,because they set upper limits for these buffers. These limits will be reached againand again if no customer demands arrive to the system for a long enough time sothat the rest of the system will have been cleared out of parts.

Many readers will recognize the system in Figure 11 as a base-stock system [34].S2 is the base-stock level of the part of the system that includes workstations WS1and WS2. Similarly, S4 is the base-stock level of the part of the system that includesworkstations WS3 and WS4.

3.1.3 On the Push/Pull and MTO/MTS Distinction

Having presented the two systems in Figures 10 and 11, we are now in a positionto comment on the push/pull and MTO/MTS distinctions. As we wrote in Section1, we adopt Definition 2 for the push/pull distinction, but we also discuss the othertwo definitions.

Based on Definitions 2 and 3, the system in Figure 10 is push, but for differentreasons; in the case of Definition 2, because production is initiated independently ofdemands, and in the case of Definition 3, because the WIP in the system is not lim-ited. Definition 1 does not cover this system. As we mentioned in Section 1, Hoppand Spearman [20], who propose Definition 3, argue that the MTO/MTS distinc-tion is orthogonal to the push/pull distinction; however, we are sure how they wouldcharacterize the system in Figure 10 in terms of the MTO/MTS distinction. In ourview, the system in Figure 10 is neither MTO nor MTS, because parts in it are nei-ther produced to meet actual customer orders (demands) nor are they produced toreplenish finished goods inventory when it is depleted by demands. This view is inline with our statement in Section 1 that, according to Definition 2, the MTO/MTSdistinction does not make sense in push systems.

The base-stock system in Figure 11, is pull, based on Definitions 1 and 2, be-cause production is driven by actual demands. Based on Definition 3, however, it ispush, because the WIP in it is not limited. Hopp and Spearman [20] propose Defi-nition 3 for the push/pull distinction as a refinement of the more general definitionthat in pull systems the release of work is authorized based on system status. Theirrefinement lies in that they restrict the system status to mean WIP. Under this def-inition of system status, the base-stock system in Figure 11 is certainly not pull. Itis important to note, however, that the production release decisions at each controlpoint of the base-stock system are based on system status, if by system status wemean the echelon inventory position of finished goods. The echelon inventory posi-tion is defined as the sum of the pending orders from the control point to the end of


the system (hence, the term “echelon”) plus the on-hand inventory of finished partsin P4 minus the backordered demands in D5. More specifically, the rule that drivesproduction release decisions in the base-stock system is that the echelon inventoryposition must always be constant and equal to the so-called “echelon base-stocklevel.” In the system in Figure 11 there are two control points: one at the entrance ofthe first stage and the other at the entrance of the second stage. For the first controlpoint, the echelon base-stock level is S2 + S4, and the pending orders are definedas the sum of the unprocessed orders in D1 plus the in-process orders in the entiremanufacturing system from WS1 thought to WS4. For the second control point, theechelon base-stock level is S4, and the pending orders are defined as the sum ofthe unprocessed orders in D3 plus the in-process orders in the second stage of themanufacturing system, namely from WS3 thought to WS4.

Based on the discussion above, we can conclude that all three definitions of thepush/pull distinction agree that in pull systems, production release decisions arebased on system status. The difference is that in Definitions 1 and 2, the systemstatus is defined as the inventory position, whereas in Definition 3, it is defined asthe WIP.

Concerning the MTO/MTS distinction, the system in Figure 11 can be charac-terized as MTO or MTS, depending on whether the echelon base-stock level is zeroor strictly greater than zero. More specifically, if the echelon base-stock level of thefirst control point is zero, i.e., if S2 + S4 = 0, which means that S2 = S4 = 0, thenany arriving customer demand will trigger the release of a raw part into the system.When this part is completed and exits WS4, it will be matched to the demand thattriggered its release, hence it will have been made to order (MTO). On the otherhand, if S4 > 0, then any arriving customer demand will be satisfied by a finishedpart from buffer P4 that has been produced before the arrival of that demand, i.e.,that has been made to stock (MTS). The arriving demand will also trigger the re-lease of a raw part into the system to replenish the inventory in buffer P4.When thispart is finished and exits WS4, it will not be matched to the demand that triggered itsrelease, but to a subsequent demand. Finally, if S4 = 0 and S2 > 0, the second stagewill be MTO (because its echelon base-stock level S4 will be zero) but the first willbe MTS (because its echelon base-stock level S2 +S4 will be strictly positive).

Of course, it is possible for a system to be partly push and partly pull, as well aspartly MTO and partly MTS. An example is the system in Figure 12. In that system,the demands that are generated by each customer demand go as far upstream as theintermediary semi-finished goods buffer, P2, instead of to the raw parts buffer P0;hence the part of the system upstream of P2 behaves like the push system in Figure10, whereas the part downstream of P2 behaves like the pull system in Figure 11.Moreover, if S3 > 0 but S4 = 0, then the part of the pull system upstream of bufferP3 is MTS, whereas the part of the system downstream of P3 is MTO.


Fig. 12 Production system with infinite-capacity buffers and demands for finished goods and semi-finished goods

3.2 Systems with WIP Control in the Presence of Demands

In this section we revisit some of the systems with WIP control that we presentedin Section 2.2, only this time in the presence of demands for finished goods. As inthe case of the systems with no WIP control that we discussed in Section 3.1, weconsider both cases where the demands for finished goods do or do not generatefurther demands for semi-finished goods and raw parts.

3.2.1 System with Demands for Finished Goods that also Generate Demandsfor Semi-Finished Goods and Raw Parts

In this section, we look at systems where the demands for finished goods also gen-erate demands for semi-finished goods and raw parts. We distinguish between twocases, one where the demands for semi-finished goods and raw parts are carried up-stream by PA cards, and another where they are transferred upstream independentlyof the PA card movement.

Systems where Demands are Carried Upstream by PA Cards

The system in Figure 13 is identical to the multi-stage sequential kanban systemin Figure 7, except that the finished goods coming out of WS4 do not immediatelyleave the system by instantaneously passing through the finished goods buffer PA4,but are stored in PA4, waiting to be matched to customer demands that arrive tobuffer D5. This means that PA4 is not always empty, as was the case in Figure 1; forthis reason it is not drawn in dotted lines.

Viewed from a different angle, the system in Figure 13 appears to be identicalto the system in Figure 10, on which a multi-stage (2-stage) kanban mechanism hasbeen superimposed to control the WIP. The truth is, however, that it is far from iden-tical. Besides the obvious difference in WIP control, there is a another fundamentaldifference between the systems in Figure 13 and Figure 10, regarding the demands.


Fig. 13 Production system with multi-stage sequential kanban control and demands for finishedgoods, semi-finished goods, and raw parts

Namely, in the system in Figure 10, each incoming customer demand for finishedgoods does not generate any further demands upstream of buffer P4; for this reason,we characterized that system as a push system. In the system in Figure 13, on theother hand, each incoming customer demand for a finished part in PA4 also gener-ates a demand for a part stored in the semi-finished goods buffer PA2 and a demandfor a raw part stored in P0, as was the case in base-stock system in Figure 11. Thesetwo demands are transferred upstream to buffers DA3 and DA1, respectively. Un-like in the base-stock system in Figure 11, however, the demands are not transferredto their respective buffers instantly upon the arrival of the customer demand thatgenerated them. Instead, they are carried upstream by the returning free PA cards(kanbans). Thus, each time a kanban is freed from buffer PA4 and is returned up-stream to buffer DA3, it carries with it a demand for a semi-finished part in PA2 and ademand for a raw part in P0. If a semi-finished part is available in PA2, then this partenters WS3, after liberating the stage-1 kanban that was attached to it and pickingup a free stage-2 kanban from buffer DA3. The demand for a semi-finished part thatwas attached to this kanban is satisfied and hence dropped. The other demand, fora raw part, that was also attached to the stage-2 kanban, is attached to the liberatedstage-1 kanban and is carried upstream to buffer DA1. The buffers of free PA cards,are denoted by DAi instead of by Ai to indicate that they contain authorization cards(“A”) attached to demands (“D”).

The system in Figure 13 is called multi-stage (sequential) kanban control systemin [6] and [34].

Figure 14 shows a system which is called integral control system in [6] and ech-elon kanban control system in [34]. It is identical to the system in Figure 9, exceptthat it is driven by customer demands for finished goods, each of which also gener-ates a demand for a part stored in the semi-finished goods buffer PA2 and a demandfor a raw part stored in P0, as was the case in the kanban system in Figure 13.

The difference with the kanban system in Figure 13 is that when a finished partleaves the finished goods buffer PA4, it releases simultaneously two kanbans: onekanban returns to buffer DA3 and the other to buffer DA1. The first kanban carrieswith it a demand for a semi-finished part in PA2 and the second carries a demandfor a raw part in P0.


Fig. 14 Production system with echelon kanban control and demands for finished goods, semi-finished goods, and raw parts

Fig. 15 Production system with CONWIP control and demands for finished goods and raw parts

Figure 15 shows a demand-driven CONWIP system. It is identical to the systemin Figure 5, except that it is driven by customer demands for finished goods, each ofwhich also generates a demand for a raw part stored in P0. It is also identical to theechelon kanban control system in Figure 14, except that the interior kanban loop ismissing.

In all the systems in Figures 13–15, the returning kanbans have two functions:firstly, they limit the WIP, and secondly, they carry the demands upstream. Based onall Definitions 1–3, all these systems would be characterized as pull, but for differentreasons; in the case of Definitions 1 and 2, because of the second function, whereasin the case of Definition 3, because of the first function.

As in case of the base-stock system in Figure 11, in both systems in Figures 13and 14, the initial state of the controlled buffers PA2 and PA4 plays the importantrole of the target inventory levels, which is played by the base-stock levels in thebase-stock system. In the kanban system in Figure 13, the initial state of buffers PA2and PA4 is equal to the number of kanbans, K1−2 and K3−4, respectively. Hence thekanbans have yet another function; that of determining the target inventory levels.In the echelon kanban control system in Figure 14, on the other hand, the initialstate of buffers PA2 and PA4 is equal to K1−4 −K3−4 and K3−4, respectively, whereclearly K1−4 ≥K3−4 (for more on the analysis of the echelon kanban control system,see [27]). Similarly, in the CONWIP system in Figure 15, the initial state of the


Fig. 16 Production system with multi-stage extended kanban control and demands for finishedgoods, semi-finished goods, and raw parts

controlled buffer PA4 is equal to K1−4 and plays the role of the target inventorylevel of finished goods.

In all three systems, the initial state of buffer PA4, i.e., the number of parts inbuffer PA4 before any customer demands arrive to the system (or, equivalently, aftera long time with no customer demand arrivals), is necessarily positive, because thisnumber is equal to the number of PA cards, which cannot be zero. This impliesthat all three systems are necessarily MTS and cannot possibly be turned into MTOby setting the respective number of kanbans equal to zero. Does this mean thatWIP control necessarily implies a MTS system and that WIP control and MTOcannot coexist? The answer is that they can coexist if the function of transferringthe demands upstream is uncoupled from the function of limiting the WIP via thekanban return movement. This is shown next.

Systems where Demands are Carried Upstream Independently of PA Cards

Figure 16 shows a system in which the demand flow is uncoupled from the kanbanflow. It combines the multi-stage kanban system in Figure 7 and the base-stocksystem in Figure 11. This system is known as a multi-stage extended kanban controlsystem and was introduced in [34]. In this system, each incoming customer demandfor finished goods also generates a demand for a part stored in the semi-finishedgoods buffer P2 and a demand for a raw part stored in P0. These two demandsare transferred upstream to buffers D3 and D1, respectively, immediately upon thearrival of the customer demand, as in the base-stock system in Figure 11.

Independently of the demands, a WIP control mechanism, which is identical tothe one in Figure 7, is also imposed on the system, separating it in two stages. Thenumber of kanbans in each stage is still K1−2 and K3−4, respectively, only now theinitial state of buffers PA2 and PA4 is not equal to the number of kanbans, as wasthe case in the demand-driven kanban system in Figure 13; instead, it is equal to the


Fig. 17 Production system with extended CONWIP control and demands for finished goods andraw parts

base-stock levels, S2 and S4, as was the case in the base-stock system in Figure 11,where S2 ≤ K1−2 and S4 ≤ K3−4. The S4 parts that are initially stored in buffer PA4have an equal number of kanbans attached to them. The rest of the kanbans, namely,K3−4 − S4 kanbans, are initially stored in buffer A3. A similar initial configurationholds in the first stage.

Just as the extended kanban control system in Figure 16 combines the multi-stagesequential kanban system in Figure 7 and the base-stock system in Figure 11, thesystem in Figure 17 combines the CONWIP system in Figure 5 and the base-stocksystem in Figure 11. For this reason, we call it extended CONWIP system. Notethat the extended CONWIP system in Figure 17 differs from the CONWIP systemin Figure 15 in that the former system uncouples the function of transferring thedemands upstream from the function of limiting the WIP via the PA card returnmovement, whereas in the latter system, the demands are transferred upstream bythe returning PA cards.

The number of PA cards (WIP cap) in the system in Figure 17 is still K1−4, aswas the case in the system in Figure 15, only now the initial state of buffer PA4 isnot equal to K1−4; instead, it is equal to the base-stock level, S4, where S4 ≤ K1−4,as was the case in the base-stock system in Figure 11. The S4 parts that are initiallystored in buffer PA4 have an equal number of kanbans attached to them. The rest ofthe kanbans, namely, K1−4 −S4 kanbans, are initially stored in buffer A1.

As was the case with the systems in Figures 13–15, the systems in Figures 16and 17 would be characterized as pull, based on all Definitions 1–3; in the case ofDefinitions 1 and 2, because production release decisions are driven by customerdemands, whereas in the case of Definition 3, because the WIP in them is limited.

If S4 > 0, both systems in Figures 16 and 17 are MTS; If S4 = 0 (and, in the caseof the extended kanban system in Figure 16, if S2 = 0 too), on the other hand, bothsystems are MTO, as was the case in the base-stock system in Figure 11.

To summarize, the extended kanban and the extended CONWIP systems are bothpull systems — in our view, because production releases are driven by demands,


Fig. 18 Production system with multi-stage sequential modified extended kanban control and de-mands for finished goods, semi-finished goods, and raw parts

as we adopt Definition 2 — with WIP control, and can be either MTO or MTS,depending on the values of the base-stock levels.

In [11], it is shown that when K1−2 = S2 and K3−4 = S4, the extended kanbansystem in Figure 16 is identical to the kanban system in Figure 14, whereas whenK1−2 = K3−4 = ∞, it is identical to the base-stock system in Figure 11. Similarly,when K1−4 = S4, the extended CONWIP system in Figure 17 is identical to theCONWIP system in Figure 15.

In [37] it is argued that setting a WIP cap in any section of a production systemmakes sense if this section and/or the section downstream of it have limited capacity.This is because releasing a part in an already congested section with limited process-ing capacity or in a section without limited processing capacity (e.g., a buffer) whichis followed by a section with limited capacity will increase the WIP in that sectionwith little or no reduction in the part’s completion time. In the multi-stage manu-facturing systems that we have been looking at, each stage consists of workstationswith limited production capacity, and all the buffers between the stages are followedby workstations with limited production capacity. The only buffer which is not fol-lowed by a workstation is the finished goods buffer. In such a system, therefore itmakes sense to set up WIP caps in all parts of the system except for the finishedgoods buffer.

With this in mind, the system in Figure 18 shows a modification of the extendedkanban control system, proposed in [37], where this idea is applied. Namely, all thestages except the last stage (in the case of Figure 18 this means the first stage only)are controlled by an extended kanban mechanism and the last stage is controlled bya generalized kanban mechanism (see, [5]), leaving the finished goods buffer PA4out of the WIP loop.

It can be shown that the system in Figure 18 behaves identically to the extendedkanban control system in Figure 16, when S4 ≤ K3−4. The advantage of the systemin Figure 18, however, is that it can also accommodate the case where S4 > K3−4,whereas the extended kanban control system in Figure 16 cannot. Therefore, the


Fig. 19 Production system with (K,S) control and demands for finished goods and raw parts

system in Figure 18 is more general than the system in Figure 16. The system inFigure 18 can also be seen as a combination of the system in Figure 8 (equivalent tothe multi-stage kanban system) and the base-stock system in Figure 11.

This idea of excluding the finished goods buffer from the WIP control loop can bealso applied to the CONWIP system. The system in Figure 19 is the same as the ex-tended CONWIP system in Figure 17, except that PA cards are released and returnedto buffer A1 when finished parts exit WS4, instead of when they exits the finishedgoods buffer, as was the case in the system in Figure 6. Liberopoulos and Dallerycall the system in Figure 19 a (K,S) system [35]. They argue that when K1−4 ≥ S4,the (K,S) system is equivalent to the extended CONWIP system in Figure 17, which,for the special case when K1−4 = S4, is equivalent to the CONWIP system in Fig-ure 15. When K1−4 = ∞, the (K,S) system is equivalent to the base-stock system.Finally, when K1−4 < S4, the (K,S) system is equivalent to a reserve-stock kanbansystem [5] or a local control policy system [6]. They also explore properties of theoptimal values of K1−4 and S4. To this end, they define a critical WIP cap level,denoted by Kc, as the smallest value of K1−4 that allows enough throughput so thatfor any K1−4 ≥ Kc, the optimal base-stock level S4 is equal to the optimal base-stocklevel that would be obtained under a pure base-stock policy, i.e., when K1−4 = ∞,for a performance criterion that minimizes the total WIP in the system subject to atype-I service level constraint. This optimal base-stock level is denoted by S∞. Theyconjecture that the overall optimal values of K1−4 and S4 are Kc and S∞.

3.2.2 Systems with Demands for Finished Goods Only

All the systems with WIP control in the presence of demands that we have seen thusfar are pull systems, because — according to Definition 2, which we adopt here —they release parts in various control points of the system in response to demand.More specifically, in all these systems, the demands for finished goods also gener-ate demands for semi-finished parts and raw part that are eventually transferred up-


Fig. 20 Production system with multi-stage sequential kanban control and demands for finishedgoods

stream the system and drive production releases. This raises the question of whetherWIP control is synonymous to pull, as Definition 3 implies.

Figure 20 shows a system that combines the multi-stage kanban system in Fig-ure 7, and the demand-driven system in Figure 10. Note that the incoming customerdemands are for finished goods only and do not generate any further demands forsemi-finished goods or raw parts. Therefore, the entire system upstream of P4 is notinformed of the demands and behaves exactly like the system in Figure 7. Namely,the production release depends on the exogenous (open-loop) controlled raw partarrival process RP as well as on the state-dependent (close-loop) WIP control im-posed by the kanban system. This type of control does not take into account theexternal disturbance that is supposed to drive the system, namely, the demand. Forthis reason, we would characterize the system as push, based on Definition 2. Wenote, however, that according to Definition 3, the system is pull, because of thestate-dependent WIP control. Definition 1 does not cover this system.

As was the case with the system in the demand-driven system in Figure 10, if the“production rate” of RP (arrival rate of raw parts) is set equal to the average demandrate, then eventually all the finished parts will be matched to demands and viceversa. In this case, the system in Figure 20 will behave like a takt-paced productionsystem with WIP control, in which the demand rate establishes the pace or takt timerather than chasing (responding to) demand [21].

If the demand arrival process were fairly invariable and the production worksta-tions well balanced (i.e., with more or less equal production rates), then the takt-paced production system in Figure 10 would perform reasonably well as it wouldresult in a relatively smooth material flow. Seen from a different point of view, atakt-paced production would help smoothen a demand which is variable, as seen bythe plant.

Hopp and Spearman [21] imply that most of the time, takt-paced productionsystems operate with backorders, i.e., with buffer D5 having a backlog of unmetorders and buffer P4 being empty. In such case, they argue that most releases canbe connected to customer orders, therefore making the system a MTO system. It


Fig. 21 Production system with CONWIP control and demands for finished goods

is for this reason that they consider the kanban system with takt time (set by theRP process) and orders (customer demands), shown in Figure 20 as an example ofMTO pull system. They also mention, however, that if the demand varies enough,then the backlog may run dry, in which case the takt-time-based system may startprereleasing jobs in a MTS or MTF mode.

We prefer to consider the system in Figure 20 as being neither MTO nor MTS,because parts are neither produced to meet specific orders (demands) nor to replen-ish finished goods inventory when it is depleted by demands. As preciously, thisview is in line with our statement in Section 1 that, according to Definition 2, theMTO/MTS distinction does not make sense in push systems. Moreover, the initialstate of the buffers does not matter, so there is no sense in talking about inventorytarget levels.

Figure 21 shows a similar system that combines the CONWIP system in Figure6, and the demand-driven system in Figure 10. Based on Definition 2, this systemtoo is push and neither MTO nor MTS.

3.2.3 Hybrid Push/Pull Systems

Real production systems combine elements of push and pull, MTO and MTS to formmore complex hybrid systems. In the figures that follow we present three examplesof hybrid systems, for illustrative purposes.

Figure 22 shows the stylized McDonald’s example used by Hopp and Spearman[21] to demonstrate the “push/pull interface,” according to Definition 3. The sys-tem is broken into two stages. The upstream stage comprises WS1 and WS2 andthe downstream stage comprises WS3 and WS4. They argue that, according to theirdefinition of the push/pull distinction, the upstream stage is a pull, because the WIPin it is controlled, whereas the downstream stage is a push, because there is limit onthe WIP, but they also argue that it is convenient to think of push as MTO and pull asMTS. In [21], they write that “push/pull interface” is a misnomer and that what theMcDonald’s system really demonstrates is that most productions systems include


Fig. 22 “McDonald’s” production system, where P0 = “refrigerator,” WS1 = “cooking,” WS2 =“assembly,” WS3 = “bagging,” WS4 = “checkout”

Fig. 23 Example of push/pull interface, where the push part has WIP control and the pull part doesnot

MTS and MTO components. We agree that the McDonald’s example demonstratesMTS/MTO interface. According Definition 2, which we adopt, the entire McDon-ald’s system is a pull system.

Figure 23 shows an example of a hybrid push/pull system with WIP control in thepush part but not in the pull part. Figure 24 shows an example of a hybrid push/pullsystem with WIP control in the pull part but not in the push part.

4 Production Control with Advance Demand Information andForecasts

In Section 3, we discussed several production control systems in the presence of de-mands, where by demands we meant customer requests for the immediate delivery


Fig. 24 Example of push/pull interface, where the pull part has WIP control and the push part doesnot

of finished goods. In practice, however, there are many cases where customers placeorders ahead of time, i.e., before they actually need the parts, hence they provide ad-vance demand information. In other cases, customers may not place orders ahead oftime, but production may be planned based on forecasts of future demands. We willlook at these two cases separately, although in many real-life production systems,hybrid combinations of both cases exist.

4.1 Systems with Advance Demand Information

The issue of advance demand information (ADI), and in particular the value of ADI,has attracted increased attention in the last decade; however, it is beyond the scopeof this chapter. An overview of recent developments and some new results can befound in the chapter by Karaesmen in this volume [22].

In this chapter, we discuss an extension of one of the systems that we presentedearlier that takes into account ADI, to demonstrate how ADI could fit in our frame-work. The system that we consider is shown in Figure 25. This system was actuallyintroduced in [37] based on previous work in [23] and [39] on how ADI can be in-corporated in the type of production control systems that we have been considering.The system in Figure 25 is identical to the modified extended kanban control sys-tem shown in Figure 18, except that each customer demand arrives to the system aconstant demand lead-time T in advance of its due date; therefore the demand for afinished part from buffer P4 is released into buffer D5, with a delay of T time unitsafter its arrival. The small solid black circles in the figure represents delays. Sim-ilarly, the demand for a semi-finished part in buffer PA2 is released into buffer D3with a delay which is determined by offsetting the demand due-date by the so-calledplanned production lead-time of the second stage, L3−4, as is done in the familiartime-phasing step of the MRP procedure. The planned production lead-time is a de-sign parameter, which plays the same role as the fixed lead-time in MRP. This means


Fig. 25 Production system with multi-stage sequential modified extended kanban control and de-mands with ADI for finished goods, semi-finished goods, and raw parts

that the demand is placed immediately, i.e., with no delay, if L3−4 ≥ T , or with adelay of T −L3−4, if L3−4 < T ; hence the delay is expressed as (T −L3−4)

+, wherewe use the notation x+ ≡ max(0,x). A further time phasing is done in the first stage,involving also the planned production lead-time of the first stage, L1−2.

The system in Figure 25 can be thought of as an MRP system operating withfirm future orders. The point to make here is that this system is still a pull system,based on Definition 2, because the release of parts into different control points ofthe system (including the entrance) is still driven by demands. Moreover, the entiresystem is still MTS if S4 > 0, because any arriving customer demand will be satisfiedby a finished part from buffer P4 that has been produced to stock, before the arrivalof that demand.

Liberopoulos and Koukoumialos [37] demonstrate numerically the tradeoff be-tween safety stock and safety time for the system in Figure 25. They show that as thedemand lead-time T increases, the optimal base-stock level of the first stage, S2, re-mains constant, while the optimal base-stock level of the second stage, S4, decreasesuntil it drops to zero, when T has reached a certain critical value. At this point, thesecond stage switches from MTS to MTO, which means that the base-stock level S4has been traded off for the demand lead-time T . As T increases beyond this criticalvalue, S2 starts decreasing too until it drops to zero, when T has reached a secondcritical value. At this point the first stage also switches from MTS to MTO, and sothe entire system operates in a MTO mode.

In the system in Figure 25, customer demands are firm orders with a demandlead-time. In many real situations, orders may not be firm, but may be subject touncertainty. For example, they may correspond to reservations or intentions to order.Liberopoulos and Koukoumialos [38] develop a model of a single-stage productionsystem with variable and uncertain ADI and numerically study its performance. Amodified version of that model is shown in Figure 26. In this system, the demandsare cancelable reservations that arrive a certain demand lead-time T before theirdue-date. A customer who places such a reservation must confirm that reservation


Fig. 26 Production system with CONWIP control and demands with uncertain ADI for finishedgoods and raw parts

∆ time units before its due date, where ∆ is called the confirmation lead-time. Thismeans that each arriving reservation must be confirmed (T −∆)+ time units afterits arrival. It is assumed that each reservation is canceled with probability q andconfirmed with probability 1 − q. Once a reservation is confirmed, it becomes afixed order and cannot be canceled. From that point on, it waits for a delay equalto min(T,∆) before it is placed into the buffer of backordered demands, D5. Thecanceled reservations are placed in a so-called canceled reservations surplus stack(CRSS).

Each arriving customer demand also generates a demand for a raw part. This de-mand is delayed by (T −L)+ time units before the decision is made as to whether itwill actually be placed in buffer D1 or be skipped, i.e., discarded; L is the plannedproduction lead-time of the entire system. Liberopoulos and Koukoumialos sug-gest that the place-or-skip decision could be made based on the contents of CRSS.Namely, if the buffer CRSS is not empty, then the demand for a raw part is skippedand the number of canceled reservations in CRSS is decreased by one. Otherwise,the demand for a raw part is placed in buffer D1. However, there could be otherways for making this decision. For example, a simple way would be to discard thedemand for a raw part with the same stationary probability q with which reservationsare canceled, and place it in buffer D1 with probability 1−q.

Again, the point to make in the system in Figure 26, is that the release of partsfor production, as complicated as it may seem, is driven by the demands. Therefore,the system remains a pull system, according to Definition 2.


Fig. 27 Production system with (K,S) control and demands for finished goods generating forecastsfor raw parts

4.2 Production Control Systems with Forecasts

A concept which is related to the concept of uncertain demand with ADI is that offorecasts of future demands. One of the traditional methods for generating forecastsis to use past demand information (time series method). Figure 27 shows a systemwhich is similar to the (K,S) system in Figure 19, except that the demand for araw part that is generated by each arriving customer demand is not immediatelytransferred to buffer D1, but is fed into a forecast generator, denoted by FG, thatgenerates forecasts of future customer demands T time units into the future, basedon past demand information. In this case, T stands for the forecast horizon. Forexample, in a discrete-time setting, the forecasted demand for discrete period t +T that is generated in period t would be some function of the actual demands inperiod t, t − 1, t − 2, · · ·. This function could be based on the well-known methodsof moving averages or exponential smoothing, for stationary demand series, or ontrend-based methods, methods for seasonal series, or the Box-Jenkins method, formore complicated series. Similar methodologies could be used in a continuous-timesetting. Regardless of the exact method used, the FG “black box” would act so asto smooth out the incoming demand stream into a less variable outgoing stream offorecasts. Naturally, on average, the forecasted demands should match the actualdemands.

Once a demand forecast for T time units into the future is generated, a demandfor a raw-part in buffer P0 is released into buffer D1 with a delay which is determinedby offsetting the forecasted demand due-date by the planned production lead-timeL, as is done in the time-phasing step of the MRP procedure.

The point to make here is that, as the release of raw parts into the system is drivenby the forecasts, which are generated by the demands, the system is still a pull sys-tem, according to Definition 2. Based on Definition 1, on the other hand, the same


system might be characterized as push, because production is initiated in anticipa-tion of future demands. Finally, based on Definition 3, it would be characterized aspull, but only because of the WIP control. If we lifted the WIP control (by settingK1−4 = ∞), the system would be push, according to Definition 3.

Concerning the MTO/MTS distinction, things are a bit less clear. Earlier, wewrote that the (K,S) control system in Figure 19 is a MTS system, if S4 > 0. Indeed,if S4 > 0, it is easy to see that the inventory position of finished goods seen at theentrance of the system, namely, the pending orders (unprocessed orders in D1 plusin-process orders in the manufacturing system from WS1 through to WS4) plus thefinished parts in P4 minus the backordered demands in D5, is always constant andequal to the base-stock level S4, which implies that the number of finished goods inbuffer P4 is always less than or equal to S4.

For the (K,S) control system with forecasts in Figure 27, on the other hand, wecannot say that the inventory position is always constant and equal to S4, becausein any given finite time interval, the number of forecasted demands that exit FGis not necessarily equal to the number of actual demands that have entered FG, asthe FG process will generally be trying to smoothen the demand arrival stream,by generating more forecasts in periods where demands arrive more sparsely, andfewer forecasts in periods where demands arrive more densely. This implies thatthe number of finished goods in buffer P4 may at times exceed S4. Still, however,the inventory position will be constant and equal to S4 on average, because as wementioned earlier, on average, the forecasted demands exiting FG should match theactual demands entering FG. With this in mind, we claim that if S4 > 0 the systemin Figure 27 is still a MTS system, because S4 is still a target for the finished goodsinventory. The difference is that in the system with forecasts, this target may notbe followed as closely as in the system without forecasts. In fact, the (K,S) controlsystem without forecasts in Figure 19 can be seen as a special case of the (K,S)control system with forecasts in Figure 27, where the forecasts generated by the FGprocess are simply equal to the demands.

Finally, if S4 = 0, the system is not a MTS. In this case, rather than describingit as a MTO system, we would characterize it as MTF; however as the forecasts aregenerated by the demands (orders) we denote this type of MTF as MTF/O.

It is noteworthy that the (K,S) control system with demand-generated forecastsin Figure 27 uses three levels of control for smoothing out the release of raw partsinto the system in order to smoothen the production flow, while still responding tothe demands. At a first level, the RP process sets the pace or takt time of produc-tion — normally, based on the long-term demand rate — by setting the pace of rawpart generation. From time to time, but with a frequency which is much lower thanthe frequency or rate of arrival of the demands, the pace of the RP process could beadjusted to better track the demand, e.g., in a situation where there is a seasonal vari-ation in the demand. At a second level, the arriving customer demands are smoothedout by the FG process; this is meant to make the arrival process that buffer D1 seesless variable than the arrival process of the actual customer demands that buffer D5sees. Finally, at a third level, the PA mechanism prohibits the release of new rawparts into the system, if K1−4 parts are already being processed in the system, even


Fig. 28 Production system with CONWIP control, demands for finished goods, and independentforecasts for raw parts

if such parts are available in P0 and demands for them are also available in D1 (WIPcontrol).

Finally, we should mention that there are other methods for generating forecastswhich do not use past demand information. For example, causal models use datafrom sources other than the series being predicted, assuming that there may be othervariables (e.g., general economic data, weather conditions, etc) with values that arelinked in some way to the demand of the product that is being forecasted. Marketingdepartments also use a variety of subjective methods for forecasting demand, basedof knowledge of the market, customer surveys, planned marketing campaigns, etc.Figure 28 shows a system which is similar to the system in Figure 27, except thatthe forecasts for finished parts and hence the forecasted demand for raw parts aregenerated independently of the demands. In this case, based on Definition 2, wewould characterize the system as a push system in which raw-parts are released ina MTF mode. As the forecasts are external and do not depend on the demands, wedenote this type of MTF as MTF/E.

Note that both systems in Figures 27 and 28 can be viewed as MRP systemsoperating with forecasts. In the system in Figure 27, these forecasts are based on theactual customer demands, and hence the MRP system is pull, based on Definition 2.In the system in Figure 28, the forecasts are independent of the demands, and hencethe system is push, based on Definition 2. The point to make here is that a systemwhich is driven by forecasts can be either pull or push, depending on whether theforecasts are generated based on the demands or not. Based on Definition 1, bothsystems would be characterized as push.


5 Conclusions

We presented several production control systems that use one or more elemen-tary mechanisms for controlling the release of parts for production. The elementarymechanisms that we considered are: 1) setting an external production pace by con-trolling the raw part arrival process, 2) authorizing releases based on the WIP inall part of the system, and 3) releasing new parts in response to actual demands orforecasts of demands.

Most of the systems that we presented are not new. We used them as a basis todiscuss several issues including the push/pull distinction and the related MTO/MTSdistinction.

Regarding the former distinction, we adopted Definition 2, which maintains thatin a pull system, production is triggered by actual demands for finished products,while in a push system, production is initiated independently of demands. We findthat this definition is clearer than the others. Having adopted this definition, weargued that the MTO/MTS distinction only makes sense for pull systems, becausepush systems disregard demand, at least in the short-term.

Definition 1, which maintains that a pull system initiates production as a reactionto present demand, while a push system initiates production in anticipation of futuredemand, seems to leave out the situation where production is initiated independentlyof demand (present or future), as in the case of a takt-paced production system (e.g.,see the systems in Figures 10, 20, and 21).

Definition 3, which states that a pull system explicitly limits the amount of WIP,while a push system has no explicit limit on WIP, does not seem to reserve a clearrole for the demand in production release control, which we think is important. Inparticular, it does not distinguish between the case where a signal authorizing therelease of a new part for production in a manufacturing system is generated when apart finishes its processing in the system, as in Figure 21, and the case where such asignal is generated when a finished part (a part which has finished its processing inthe system) is consumed by a demand, as in Figure 15.

Much has been written in the literature about the benefits of pull, but little aboutpush — usually in relation to pull. In fact, the term “push” might have never beenbrought up if it were not for pull. As we mentioned in the introduction, our goal wasnot to provide the “right” answers to the push/pull and MTO/MTS questions, butrather to clarify what these questions are, by precisely describing different systemsand pointing out their similarities and differences.

Although we adopted Definition 2 for the push/pull distinction, we agree withHopp and Spearman [20], who propose Definition 3, that the key benefits of a pullsystem (the “magic of pull” as they call it) arise when it establishes a WIP limit.Indeed, the (K,S) system in Figure 19, which is a pull system with a WIP cap,includes the base-stock system (i.e., the same system but without the WIP cap) as aspecial case, and so it clearly performs better than it. It also includes the CONWIPsystem in Figure 15 as a special case, and so it performs better than that system too.This observation alone points to the potential benefits of uncoupling the transfer ofdemands from the kanban return movement used to limit the WIP.


It would also be interesting to see how the (K,S) system in Figure 19, performsrelatively to the “push version” of the CONWIP system in Figure 19, where by pushwe mean that production is triggered independently of demand, based on Definition2. Clearly, the two systems can achieve the same maximum throughput, which co-incides with the throughput of the demand-ignoring version of the system shown inFigure 6. When the two systems are driven by demands, intuitively, the (K,S) systemshould perform better, because, in its decision to release new parts for production,it takes into account not only the WIP in the system, as is the case in system inFigure 21, but also the inventory position of the finished goods buffer. Showing thesuperiority of the (K,S) system over the push CONWIP system would demonstratea different “magic of pull,” namely the benefits that arise when a WIP controlledsystem releases parts for production in response to demands, instead of ignoringdemands.

Finally, an interesting generalization of the (K,S) system in Figure 19, is the(K,S) system with demand-based forecasts in Figure 27. As was mentioned in Sec-tion 4.2, the former system is a special case of the latter system if the forecastsgenerated by the FG process are simply equal to the demands. The latter systemhas a rich set of controls (production pace, WIP control, MTO, MTF, and MTS) fortrying to achieve good customer service with a smoother and less costly produc-tion flow. Exploring the interplay between these controls would be a promising andchallenging direction for future research.

Acknowledgements The work in this chapter was supported by grant MIS 379526 “Odysseus: Aholistic approach for managing variability in contemporary global supply chain networks,” whichwas co-financed by the European Union (European Social Fund - ESF) and Greek national fundsthrough the Operational Program “Education and Lifelong Learning” of the National StrategicReference Framework (NSRF) - Research Funding Program: THALES: Reinforcement of the in-terdisciplinary and/or inter-institutional research and innovation.

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Index

(K,S) control system, 24, 31, 32, 34

advance demand information, 28–30

base-stock system, 15, 16blocking before service with position

non-occupied, 8bottleneck workstation, 6

classical kanban system, 9confirmation lead-time, 30controlled token-based system, 12CONWIP system, 10, 20, 34

demand lead-time, 28, 29

echelon base-stock level, 17echelon kanban control system, 12, 13, 19, 20extended CONWIP system, 22extended kanban control system, 21, 22

flow line with finite-capacity buffers, 7, 8forecast generator, 31forecasts, 31–33

hybrid push/pull, 9, 17, 26–28

integral control system, 13, 19

inventory position, 16, 17, 32

local control policy, 24

make-to-forecast, 4, 32, 33make-to-order, 4, 16, 17, 23, 32, 34make-to-stock, 4, 16, 17, 23, 32, 34modified extended kanban control system, 23MRP system, 28, 29, 31, 33multi-loop CONWIP system, 11multi-stage kanban system, 11, 18, 19, 25

planned production lead-time, 28production authorization card, 8pull, 2–4, 9–11, 16, 17, 22, 34push, 2–4, 16, 17, 25, 34

raw part arrival process, 6reserve-stock kanban system, 24

synchronization station, 8, 14

takt-paced production system, 25, 34

WIP cap, 3, 9–11WIP constant, 9–11WIP limit, 7, 9, 10, 34

39

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