scispace - formally typeset

Journal ArticleDOI

On the backlog-sequencing decision for extending the applicability of ConWIP to high-variety contexts:an assessment by simulation

19 Jan 2017-International Journal of Production Research (Taylor & Francis)-Vol. 55, Iss: 16, pp 4695-4711

TL;DR: Using simulation, it is demonstrated that the choice of backlog-sequencing rule significantly impacts throughput times and tardiness-related performance measures, and capacity slack-based sequencing rules achieve significant performance improvements over ‘classical’ ConWIP backlog- sequencing rules.

AbstractConstant Work-in-Process (ConWIP) is a card-based control system that was developed for simple flow shops – a lack of load-balancing capabilities hinders its application to more complex shops. In contrast, load balancing is an integral part of Workload Control, a production planning and control concept developed for high-variety environments. One means of load balancing evident in the Workload Control literature is through the use of a capacity slack-based backlog-sequencing rule. This study therefore investigates the potential of the backlog-sequencing decision to improve load balancing in the context of ConWIP, thereby making it suitable for more complex, high-variety environments. Using simulation, we demonstrate that: (i) the choice of backlog-sequencing rule significantly impacts throughput times and tardiness-related performance measures; and (ii) capacity slack-based sequencing rules achieve significant performance improvements over ‘classical’ ConWIP backlog-sequencing rules. These results signifi...

Topics: CONWIP (67%), Load balancing (computing) (53%), Production planning (50%)

Summary (5 min read)

1. Introduction

  • Constant Work-in-Process (ConWIP; e.g. Spearman et al., 1990; Hopp & Spearman, 2001) is a simple card-based production control system.
  • ConWIP’s WIP-Cap restricts the work-in-process released to the shop floor but it does not balance the workload on the shop floor across resources if, for example, processing times, routings, and/or the occurrence of demand follow a stochastic process.
  • An alternative approach for improving load balancing has been presented in the Workload Control literature in the form of the “backlog-sequencing decision” (Philipoom et al., 1993; Fredendall et al., 2010; Thürer et al., 2015, 2016b).
  • Specifically, the capacity slack-based backlog-sequencing rule proposed by Philipoom et al. (1993) was shown by Thürer et al. (2015) to have much promise.
  • The results are then presented and discussed in Section 4.

2. Literature Review – Backlog-Sequencing Rules

  • ConWIP, as illustrated in Figure 1, is arguably the simplest card-based control system available in the literature.
  • Once a job leaves the system, its card is freed and can be used by a different job from the set of jobs waiting to enter the system.
  • The decision concerning which job(s) to release next is called the “backlogsequencing decision”.
  • Other means of bringing about an improvement, such as by changing the loop structure, do not apply since they would transform ConWIP into a different system altogether; see, e.g.
  • This section does not aim to present a complete review of the ConWIP (or Workload Control) literature; rather, it focuses on identifying the backlog-sequencing rules to be considered in their study.

2.1 Backlog-sequencing Rules from the ConWIP Literature

  • Many papers that apply ConWIP do not specify which backlog-sequencing rule is incorporated (e.g. Spearman et al., 1990; Germs & Riezebos, 2010).
  • It appears that this aspect of the system was either not specified (or overlooked) or it was assumed that this did not have a significant impact on performance.
  • Earliest Due Date (EDD), a time-oriented rule that sequences jobs according to their due date.
  • One load-oriented rule that has been applied in the ConWIP literature is as follows: Shortest Total Work Content (STWK), a load-oriented rule that sequences jobs according to the sum of all processing times in the routing of an order.

2.2 Capacity Slack-based Sequencing from the Workload Control Literature

  • Fredendall et al. (2010) and Thürer et al. (2015) recently demonstrated the potential for performance improvement from using a backlog-sequencing rule developed by Philipoom et al. (1993) – the Capacity Slack (CS) rule – in combination with Workload Control order release.
  • This corrected aggregate load method (Oosterman et al., 2000) recognizes that an order’s contribution to a station’s direct load is limited to only the proportion of time that an order is at the station.
  • In contrast to Workload Control, ConWIP does not measure workloads in full processing times or corrected processing times, but in terms of the number of jobs.
  • Capacity Slack (CS), which uses full processing times to calculate the capacity slack S j (Equation 2).

2.2.1 Modified Capacity Slack-based Backlog-Sequencing Rules

  • The Workload Control literature suggests that capacity slack-based backlog-sequencing rules can be a powerful means of improving load balancing.
  • Thürer et al. (2015) recently demonstrated that a sole focus on load balancing can be detrimental to performance, since large but urgent orders may never be released.
  • Then, within the class of non-urgent jobs, jobs are sequenced according to the PRD rule.
  • Since the authors have four different capacity slack rules in this study depending on how the workload is measured (Equations 2 to 5 above), four different modified capacity slack rules will be considered: MODCS, MODCSCor, MODCSjob, and MODCSjobdir.
  • Overall, twelve different backlog-sequencing rules will be considered (i.e. four rules from the ConWIP literature, four CS rules, and four modified CS rules), as will be summarized in Section 3.2 below.

3. Simulation Model

  • The shop and job characteristics modeled in the simulations are first outlined in Section 3.1.
  • Section 3.2 then describes how ConWIP and the backlog-sequencing rules have been operationalized in the simulations.
  • The priority dispatching rules applied on the shop floor are then discussed in Section 3.3.
  • Finally, the experimental design is outlined and the measures used to evaluate performance are presented in Section 3.4.

3.1 Overview of Modeled Shop and Job Characteristics

  • A simulation model of a general flow shop (Oosterman et al., 2000) has been implemented using ARENA simulation software.
  • The model is stochastic, whereby job routings, processing times, inter-arrival times and due dates are stochastic variables.
  • The shop contains six stations, where each station is a single constant capacity resource.
  • As in previous studies on ConWIP, the authors consider the output to be fixed, thereby neglecting options for adjusting capacity (and thus the output rate), although this may often be a pre-requisite for the implementation of pull systems in practice.
  • While any individual high-variety shop in practice will differ in many aspects to this stylized environment, it captures the typical shop characteristics of high routing variability, processing time variability, and arrival variability.

3.2 ConWIP

  • As in previous simulation studies on ConWIP (e.g. Hopp & Spearman, 1991; Bonvik et al., 1997; Herer & Masin, 1997; Jodlbauer & Huber, 2008), it is assumed that materials are available and all necessary information regarding due date, shop floor routing and processing times is known upon the arrival of an order in the pool.
  • Whenever the number of jobs on the shop floor is below a pre-established limit (WIP-Cap), jobs in the pool are sequenced according to the applied backlog-sequencing rule, and the next job in the sequence is released to the shop floor.
  • Ns that is used when calculating the priority measure for capacity slack-based backlog-sequencing rules changes according to the limit that is applied.
  • Table 2 summarizes the twelve backlog-sequencing rules considered in this study and lists the parameters used for each.

3.3 Priority Dispatching Rule for the Shop Floor

  • ConWIP controls the work released to the shop floor, but it does not control the flow of work on the shop floor.
  • The calculation of the operation due date δij for the i th operation of a job j follows Equation (6) below.
  • This allowance is given by the running average of the actually realized operation throughput times at each station.
  • Finally, the MODD rule prioritizes jobs according to the lowest priority number, which is given by the maximum of the operation due date and earliest finish time.

3.4 Experimental Design and Performance Measures

  • (i) the six different levels of the number of jobs (or cards) allowed in the system; (ii) the 12 different backlog-sequencing rules; and, (iii) the four dispatching rules (FSFS, ODD, SPT, and MODD), also known as The experimental factors are.
  • A full factorial design with 288 scenarios was used, where each scenario was replicated 100 times.
  • These parameters allowed us to obtain stable results while keeping the simulation run time to a reasonable level.
  • In addition to these four main performance measures, the authors also measure the shop floor throughput time as an instrumental performance variable.
  • This approach was introduced by Oosterman et al. (2000) and has been adopted in many subsequent studies on load-based order release (e.g. Germs & Riezebos, 2010; Thürer et al. 2012).

4. Results

  • Statistical analysis has been conducted by applying ANOVA to give a first indication of the relative impact of their three experimental factors: the backlog-sequencing rule, the dispatching rule, and the number of jobs (or cards) allowed in the system.
  • The results are summarized in Table 3; all main effects, two-way interactions and three-way interactions are shown to be statistically significant.
  • The Scheffé multiple-comparison procedure was used to examine the significance of the differences between the outcomes of the individual backlog-sequencing and dispatching rules.
  • Further, their four dispatching rules perform statistically different for all performance measures considered.
  • The robustness of the results to changes in the shop floor dispatching rule is then assessed in Section 4.2.

4.1 Performance Assessment of Backlog-sequencing Rules

  • To aid interpretation, the simulation results are presented in the form of performance curves.
  • The left-hand starting point of the curves represents the lowest number of jobs (or cards) allowed (30 jobs).
  • Increasing the number of jobs in the system increases the level of work-in-process and, as a result, increases shop floor throughput times.
  • Meanwhile, under infinite norms, jobs are not withheld in the pool meaning the backlog-sequencing rule is inactive, which results in all backlog-sequencing rules converging on the same point.
  • Figures 2a and 2b show the total throughput time, percentage tardy, mean tardiness and standard deviation of lateness results over the shop floor throughput time results for the ‘classical’ ConWIP backlog-sequencing rules (from Section 2.1) and for the capacity slack-based backlog-sequencing rules (from Section 2.2), respectively.

4.1.1 Classical ConWIP Backlog-sequencing Rules (Figure 2a)

  • PRD considers the routing length, i.e. the number of stations in the routing of jobs.
  • As a result, the more stations there are in the routing of a job, the higher the priority of the job among jobs with similar due dates.
  • This explains the increase in the percentage tardy and mean tardiness as the standard deviation of lateness is similar across PRD, EDD, and FCFS.
  • ConWIP’s release function does not consider job characteristics – the next job is simply released regardless of its characteristics (e.g. its routing or work content) if a card is available.
  • Finally, typical shortest processing time effects can be observed for STWK, resulting in the shortest total throughput times and lowest percentage tardy across the four classical ConWIP rules (FCFS, EDD, PRD, and STWK).

4.1.2 Capacity Slack-based Sequencing Rules (Figure 2b)

  • The best performance in terms of the total throughput time, mean tardiness, and the standard deviation of lateness is realized by CSjobdir, which uses the direct load queuing at each station (measured in terms of the number of jobs) to calculate the capacity slack.
  • It appears that CSjobdir, which only considers the direct load queuing at a station, is more able to realize load balancing in the context of ConWIP than rules that consider both the direct load and the indirect load (i.e. the load on its way to a station).
  • Similarly, a job may or may not have a particular station in its routing.
  • Ws at each station exceeds the limit Ns and where the ratio between the load contribution and load gap elements is substituted by M, where M is a sufficiently large number.
  • Another interesting result is the poor performance of the modified capacity slack-based rules.

4.2 Sensitivity Analysis: The Impact of the Shop Floor Dispatching Rule

  • As expected, ODD dispatching improves the percentage tardy, mean tardiness, and the standard deviation of lateness performance compared to FSFS dispatching.
  • This can be observed from Figures 3a and 3b, which show the total throughput time, percentage tardy, mean tardiness and standard deviation of lateness results over the shop floor throughput time results under ODD dispatching for the ‘classical’.
  • ConWIP backlog-sequencing rules and the capacity slack-based backlog-sequencing rules, respectively.
  • But the SPT effects created by the dispatching rule are so strong that the performance differences across backlog-sequencing rules become arguably negligible.
  • MODD switches between SPT dispatching and ODD dispatching.

5. Conclusions

  • Constant Work-In-Process is a simple card-based control system.
  • Based on this finding from the Workload Control literature, the authors have asked: Using a simulated general flow shop environment with high variability in terms of job arrivals, processing times, and routings, they have shown that the backlogsequencing decision has a significant impact on the performance of ConWIP.
  • More specifically, it has been shown that capacity slack-based rules maintain their ability to significantly improve load balancing.
  • Thus, they provide an important means of extending the applicability of ConWIP to more complex high-variety contexts.

Did you find this useful? Give us your feedback

...read more

Content maybe subject to copyright    Report

1
On the Backlog-Sequencing Decision for Extending the
Applicability of ConWIP to High-Variety Contexts: An
Assessment by Simulation
Matthias Thürer (corresponding author), Nuno O. Fernandes, Mark Stevenson and Ting Qu
Name: Prof. Matthias Thürer
Institution: Jinan University
Address: Institute of Physical Internet
School of Electrical and Information Engineering
Jinan University (Zhuhai Campus)
519070, Zhuhai, PR China
E-mail: matthiasthurer@workloadcontrol.com
Name: Prof. Nuno O. Fernandes
Institution: Instituto Politécnico de Castelo Branco
Address: Av. do Empresário, 6000-767
Castelo Branco - Portugal
E-mail: nogf@ipcb.pt
Name: Prof. Mark Stevenson
Institution: Lancaster University
Address: Department of Management Science
Lancaster University Management School
Lancaster University
LA1 4YX - U.K.
E-mail: m.stevenson@lancaster.ac.uk
Name: Prof. Ting Qu
Institution: Jinan University
Address: Institute of Physical Internet
School of Electrical and Information Engineering
Jinan University (Zhuhai Campus)
519070, Zhuhai, PR China
E-mail: quting@jnu.edu.cn
Keywords: Constant Work-in-Process (ConWIP); make-to-order (MTO) production;
dispatching; Workload Control; backlog-sequencing rule.

2
On the Backlog-Sequencing Decision for Extending the
Applicability of ConWIP to High-Variety Contexts: An
Assessment by Simulation
Abstract
Constant Work-in-Process (ConWIP) is a card-based control system that was developed for
simple flow shops a lack of load balancing capabilities hinders its application to more
complex shops. In contrast, load balancing is an integral part of Workload Control, a
production planning and control concept developed for high-variety environments. One
means of load balancing evident in the Workload Control literature is through the use of a
capacity slack-based backlog-sequencing rule. This study therefore investigates the potential
of the backlog-sequencing decision to improve load balancing in the context of ConWIP,
thereby making it suitable for more complex, high-variety environments. Using simulation,
we demonstrate that: (i) the choice of backlog-sequencing rule significantly impacts
throughput times and tardiness related performance measures; and, (ii) capacity slack-based
sequencing rules achieve significant performance improvements over ‘classical’ ConWIP
backlog-sequencing rules. These results significantly extend the applicability of ConWIP.
Results from the Workload Control literature however do not directly translate across to
ConWIP. The simplified release procedure of ConWIP makes backlog-sequencing based on
planned release dates dysfunctional. This negatively impacts the performance of modified
capacity slack-based sequencing rules that were recently shown to be the best choice for
Workload Control.
Keywords: Constant Work-in-Process (ConWIP); make-to-order (MTO) production;
dispatching; Workload Control; backlog-sequencing rule.

3
1. Introduction
Constant Work-in-Process (ConWIP; e.g. Spearman et al., 1990; Hopp & Spearman, 2001) is
a simple card-based production control system. It is essentially a pull system (Hopp &
Spearman, 2004) that uses a so-called Work-In-Process (WIP) limit or cap (WIP-Cap) that is
pre-established by management to realize input/output control (Wight, 1970; Plossl & Wight,
1971). In accordance with input/output control, the output of work from the shop floor
determines the input of work to the shop floor from a so-called pre-shop pool or ‘backlog’ (in
Spearman et al., 1990). Jobs are only permitted to enter the shop floor if the WIP-Cap is not
violated; otherwise, they form a ‘backlog’ and have to wait in the pre-shop pool until some of
the jobs on the shop floor have been completed. Cards circulate between the shop floor and
the pool; and the return of a card signals that a job has been completed.
ConWIP is a simple means of exercising pull control, providing that product variety is
restricted its applicability to high-variety make-to-order environments is rather limited
(Thürer et al., 2016a). A key reason for this is its lack of load balancing capabilities (Germs
& Riezebos, 2010). Load balancing is here defined as a leveling of the workload across
resources. ConWIP’s WIP-Cap restricts the work-in-process released to the shop floor but it
does not balance the workload on the shop floor across resources if, for example, processing
times, routings, and/or the occurrence of demand follow a stochastic process. In this context,
tools for load balancing, such as line balancing and task analysis, which presuppose a certain
degree of repetitiveness, do not apply. An alternative approach for improving load balancing
has been presented in the Workload Control literature in the form of the “backlog-sequencing
decision” (Philipoom et al., 1993; Fredendall et al., 2010; Thürer et al., 2015, 2016b).
Workload Control and its card-based variant, Control of Balance by Card Based Navigation
(COBACABANA: Land, 2009; Thürer et al., 2014) is an alternative production planning
and control system to ConWIP that was developed for high-variety contexts (Stevenson et al.,
2005). In contrast to ConWIP, Workload Control incorporates load balancing as part of its
workload limiting strategy. Yet Thürer et al. (2015) recently demonstrated that load
balancing can and should be enhanced using an appropriate backlog-sequencing rule to
influence the sequence in which jobs are considered for release. Specifically, the capacity
slack-based backlog-sequencing rule proposed by Philipoom et al. (1993) was shown by
Thürer et al. (2015) to have much promise. It is therefore argued here that load balancing
should be embedded within ConWIP in the form of an appropriate backlog-sequencing rule;
and that doing so will extend the scope and applicability of this important card-based system.

4
While the importance of the so-called backlog-sequencing problem has been recognized
in some of the ConWIP literature, previous studies have often focused on complex
optimization algorithms (e.g. Woodruff & Spearman, 1992; Herer & Masin, 1997; Golany et
al., 1999; Framinan et al., 2001; Zhang & Chen, 2001; Cao & Chen, 2005). In this body of
work, a fixed set of orders has been assumed and the sequence in which these orders should
be released by a ConWIP system to optimize a certain performance parameter has been
determined. However, in a make-to-order system, where job arrivals follow a stochastic
process, jobs may arrive at any moment in time. As a consequence, not only does the
optimization algorithm need to be executed at each release instance, but a so-called optimal
solution may turn out to be far from optimal when a new job arrives that needs to be
incorporated into the existing schedule. Therefore, we agree with Lingayat et al. (1995) that a
greedy heuristic, i.e. a simple backlog-sequencing rule, represents a more feasible solution
than optimization for this context. The main prior study on simple sequencing rules was
presented by Leu (2000), but this contribution does not reflect recent advances, such as the
emergence of capacity slack-based sequencing rules. In response, we ask:
Can a backlog-sequencing rule be used to extend the applicability of ConWIP to
high-variety make-to-order flow shops?
An exploratory study based on controlled simulation experiments is used to provide an
answer to this question. We will show that specifically capacity-slack based backlog
sequencing rules have the potential to improve performance compared to ‘classical’ ConWIP
backlog-sequencing rules.
The remainder of this paper is organized as follows. The literature is first reviewed to
identify the backlog-sequencing rules available in the ConWIP and Workload Control
literatures in Section 2. Here we also use the capacity slack-based rules from the Workload
Control literature as the basis for the design of new capacity slack-based rules that reflect the
particular characteristics of ConWIP. Section 3 then outlines the simulation model that is
used to examine the performance impact of improved backlog-sequencing in a high variety
context (in terms of job arrival times, processing times, and routings). The results are then
presented and discussed in Section 4. Finally, concluding remarks are made in Section 5,
where managerial implications and future research directions are also outlined.

5
2. Literature Review Backlog-Sequencing Rules
ConWIP, as illustrated in Figure 1, is arguably the simplest card-based control system
available in the literature. Whenever the number of jobs in the system (or shop floor) is below
a pre-established limit, a new job is released to the system. To control the number of jobs,
each job in the system has to have a ConWIP card attached to it. Thus, by restricting the
number of cards that can circulate in the system, the number of jobs is also restricted. Once a
job leaves the system, its card is freed and can be used by a different job from the set of jobs
waiting to enter the system. The place where these jobs (the ‘backlog’) wait is referred to as a
pool. The decision concerning which job(s) to release next is called the backlog-
sequencing decision.
[Take in Figure 1]
ConWIP is a simple means of exercising pull control, providing that product variety is
restricted. Indeed, Hopp & Spearman (2001, p. 461) argued that ConWIP only works well if
routings are constant and processing time variability is low. The main mean of improving the
performance of ConWIP is by changing the sequence in which jobs are released to the shop
floor. Other means of bringing about an improvement, such as by changing the loop structure,
do not apply since they would transform ConWIP into a different system altogether; see, e.g.
Thürer et al. (2016a) for a discussion on alternative card-based control systems. Further, this
study focuses on ConWIP and on a balanced shop since in a shop with stationary
bottleneck(s) load balancing across resources is less important. ConWIP extensions, such as
ConWork or ConLoad (Rose, 1999), which presuppose a stationary bottleneck, are therefore
not considered.
This section does not aim to present a complete review of the ConWIP (or Workload
Control) literature; rather, it focuses on identifying the backlog-sequencing rules to be
considered in our study. For a broader review of ConWIP, the reader is referred to Framinan
et al. (2003) and Prakash & Chin (2015). Our review hereby focuses on the limited number of
greedy heuristics available in the ConWIP and Workload Control literature. This is motivated
by the fact that the backlog-sequencing rule must be suitable for high-variety make-to-order
contexts where processing times, routings, and the inter-arrival times of orders follow a
stochastic process. This setting means that a significant part of the literature that assumes a
given set of jobs, which are optimized for a certain set of performance measures are omitted
as not being relevant because job arrivals follow a stochastic process. Approaches based on
linear/non-linear integer programming (e.g. Herer & Masin, 1997; Luh et al., 2000; Zhang &

Citations
More filters

Journal ArticleDOI
TL;DR: This paper aims to update the last ConWip systematic review that dates back to 2003 and to provide a guide for understanding through an original classification method that enables the differentiation of papers that concentrate onConWip sizing, performance and context as well as a comparison with other PCSs.
Abstract: In the past decade, a growing body of literature has investigated the CONstant Work In Progress (ConWip) production control system (PCS). In view of the current industrial challenges entailing adaptability, product customisation, decreasing leadtimes and customer satisfaction, ConWip seems to be an effective and adaptive PCS for manufacturers. This paper aims to update the last ConWip systematic review that dates back to 2003 and to provide a guide for understanding through an original classification method. This method enables the differentiation of papers that concentrate on ConWip sizing, performance and context as well as a comparison with other PCSs. In addition to providing a key to interpreting the research approaches, the criteria considered answers questions on how to implement, how to optimise and why and when to use ConWip. Finally, the most relevant research avenues are highlighted to provide future lines of research.

27 citations


Cites background from "On the backlog-sequencing decision ..."

  • ...Tardif and Maaseidvaag (2001) CC Thürer et al. (2017) T Wang, Cao, and Kong (2009) CC T Zhang and Chen (2001) LS International Journal of Production Research 5739...

    [...]


Journal ArticleDOI
TL;DR: An essential task in manufacturing planning and control is to determine when to release orders to the shop floor, and a prominent approach is the workload control (WLC) concept.
Abstract: An essential task in manufacturing planning and control is to determine when to release orders to the shop floor. A prominent approach is the workload control (WLC) concept which originated from th...

12 citations


Cites background or methods from "On the backlog-sequencing decision ..."

  • ...…in unbalanced shops: the study by Fernandes, Land, and Carmo-Silva (2014) focuses on a job shop, Chen et al. (2019) analyses a general flow shop and Thuerer et al. (2017b) includes both a pure job shop and a general flow shop (as defined in Oosterman, Land, and Gaalman 2000) in their simulation…...

    [...]

  • ...Furthermore, research on semiconductor wafer fabs has neglected pool sequencing rules, although several WLC studies highlighted their important influence on performance within rule based order release models (Thuerer et al. 2015, 2017a)....

    [...]

  • ...Thus, all non-bottleneck-products are released immediately at the moment of arrival in the order pool (Thuerer et al. 2017b)....

    [...]

  • ...Additionally, research on rule based WLC in SME-MTO has stressed the important role of pool sequencing rules on the performance of rule based order release models (LUMS and ConWIP; e.g. Thuerer et al. 2015, 2017a)....

    [...]


Journal ArticleDOI
TL;DR: It is demonstrated that the original procedure works well in a pure flow shop, but becomes dysfunctional in a general flow shop where job routings vary and performance can be significantly enhanced by switching from a focus on urgency to afocus on the shortest bottleneck processing time during periods of high load.
Abstract: One of the main elements of the theory of constraints is its Drum–Buffer–Rope (DBR) scheduling (or release) mechanism that controls the release of jobs to the system. Jobs are not released directly...

10 citations


Journal ArticleDOI
TL;DR: This study assesses the performance of COBACABANA and POLCA in a high‐variety make‐to‐order shop and argues that the different mechanisms may play complementary rather than competing roles.
Abstract: Material flow control mechanisms determine: (i) whether an order should be released onto the shop floor; and (ii) whether a station should be authorized to produce. Well‐known approaches include Kanban, Drum‐Buffer‐Rope (DBR), Constant Work‐in‐Process (ConWIP), Paired‐cell Overlapping Loops of Cards with Authorization (POLCA), Workload Control (WLC), and Control of Balance by Card Based Navigation (COBACABANA). The literature typically treats these approaches as competing, meaning studies argue for the superiority of one over another. However, a closer look reveals that existing mechanisms either focus on order release (ConWIP, DBR, WLC, and COBACABANA) or on production authorization (Kanban and POLCA). This study therefore calls for a paradigm shift and argues that the different mechanisms may play complementary rather than competing roles. Using simulation, we assess the performance of COBACABANA and POLCA in a high‐variety make‐to‐order shop, a type of shop arguably in most need of material flow control given the importance of throughput times and delivery time adherence. Results demonstrate that COBACABANA outperforms POLCA, but the simultaneous adoption of both control mechanisms outperforms the use of either one in isolation. More specifically, adding POLCA production authorization to COBACABANA order release enables the superfluous direct load to be further reduced, resulting in shop floor throughput time reductions of between 15% and 26% while further reducing the percentage tardy and mean tardiness by up to 14%. Compared to no material flow control, the new combined mechanism realizes a reduction of almost 50% in the percentage tardy and more than 30% in mean tardiness.

9 citations


Journal ArticleDOI
TL;DR: The study finds that hybrid Kanban-ConWIP pull production control policies have a better performance in controlling the studied multi-layer multi-stage manufacturing and assembly system.
Abstract: Purpose: Just-In-Time (JIT) production has continuously been considered by industrial practitioners and researchers as a leading strategy for the yet popular Lean production. Pull Production Control Policies (PPCPs) are the major enablers of JIT that locally control the level of inventory by authorizing the production in each station. Aiming to improve the PPCPs, three authorization mechanisms: Kanban, constant-work-in-process (ConWIP), and a hybrid system, are evaluated by considering uncertainty. Design/methodology/approach: Multi-Criteria Decision Making (MCDM) methods are successful in evaluating alternatives with respect to several objectives. The proposed approach of this study applies the fuzzy set theory together with an integrated Analytical Hierarchy Process (AHP) and a Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. Findings: The study finds that hybrid Kanban-ConWIP pull production control policies have a better performance in controlling the studied multi-layer multi-stage manufacturing and assembly system. Practical implications: To examine the approach a real case from automobile electro mechanical part production industry is studied. The production system consists of multiple levels of manufacturing, feeding a multi-stage assembly line with stochastic processing times to satisfy the changing demand. Originality/value: This study proposes the integrated Kanban-ConWIP hybrid pull control policies and implements several alternatives on a multi-stage and multi-layer manufacturing and assembly production system. An integrated Fuzzy AHP TOPSIS method is developed to evaluate the alternatives with respect to several JIT criteria.

9 citations


Cites background from "On the backlog-sequencing decision ..."

  • ...On the other hand, the operation -162- Kanban circulates in between the production stations when there is an actual demand to process (Thürer et al., 2017)....

    [...]

  • ...Kanban circulates in between the production stations when there is an actual demand to process (Thürer et al., 2017)....

    [...]

  • ...In a recent study (Thürer et al. 2017) the difference between WIP Kanbans and operation Kanbans was clerified....

    [...]


References
More filters

Book
01 Aug 1995
Abstract: PART I THE LESSONS OF HISTORY1. Manufacturing in America 2. Inventory Control: From EOQ to RDP 3. The MRP Crusade4. The JIT Revolution5. What Went WrongPART II FACTORY PHYSICS6. A Science of Manufacturing7. Basic Factory Dynamics8. Variability Basics9. The Corrupting Influence of Variability10. Push and Pull Production Systems 11. The Human Element in Operations Management 12. Total Quality Manufacturing PART III PRINCIPLES IN PRACTICE13. A Pull Planning Framework 14. Shop Floor Control15. Production Scheduling 16. Aggregate and Workforce Planning 17. Supply Chain Management 18. Capacity Management 19. Synthesis-Pulling It All Together References Index

1,290 citations


"On the backlog-sequencing decision ..." refers methods in this paper

  • ...In addition to First in System First Served (FSFS) dispatching, which was suggested by Spearman, Woodruff, and Hopp (1990) and Hopp and Spearman (2001) and is used as a baseline measure in this study, three alternative dispatching rules will be considered: (i) the Operation Due Date (ODD) rule;…...

    [...]

  • ...Constant Work-in-Process (ConWIP; e.g. Spearman, Woodruff, and Hopp 1990; Hopp and Spearman 2001) is a simple card-based production control system....

    [...]


Journal ArticleDOI
Abstract: SUMMARY This paper describes a new pull-based production system called CONWIP. Practical advantages of CONWIP over push and other pull systems are given. Theoretical arguments in favour of the system are outlined and simulation studies are included to give insight into the system's performance.

891 citations


"On the backlog-sequencing decision ..." refers background or methods in this paper

  • ...In addition to First in System First Served (FSFS) dispatching, which was suggested by Spearman, Woodruff, and Hopp (1990) and Hopp and Spearman (2001) and is used as a baseline measure in this study, three alternative dispatching rules will be considered: (i) the Operation Due Date (ODD) rule;…...

    [...]

  • ...In accordance with input/output control, the output of work from the shop floor determines the input of work to the shop floor from a so-called pre-shop pool or ‘backlog’ (in Spearman, Woodruff, and Hopp 1990)....

    [...]

  • ...2.1 Backlog-sequencing rules from the ConWIP literature Many papers that apply ConWIP do not specify which backlog-sequencing rule is incorporated (e.g. Spearman, Woodruff, and Hopp 1990; Germs and Riezebos 2010)....

    [...]

  • ...Constant Work-in-Process (ConWIP; e.g. Spearman, Woodruff, and Hopp 1990; Hopp and Spearman 2001) is a simple card-based production control system....

    [...]


01 Jan 2004
Abstract: The terms pull and lean production have become cornerstones of modern manufacturing practice. However, although they are widely used, they are less widely understood. In this paper, we argue that while the academic literature has steadily revealed the richness of the pull/lean concepts, the practitioner literature has progressively simplified these terms to the point that serious misunderstandings now exist. In hopes of reducing confusion, we offer general, but precise definitions of pull and lean. Specifically, we argue that pull is essentially a mechanism for limiting WIP, and lean is fundamentally about minimizing the cost of buffering variability.

480 citations


Journal ArticleDOI
TL;DR: It is argued that pull is essentially a mechanism for limiting WIP, and lean is fundamentally about minimizing the cost of buffering variability.
Abstract: The terms pull and lean production have become cornerstones of modern manufacturing practice. However, although they are widely used, they are less widely understood. In this paper, we argue that while the academic literature has steadily revealed the richness of the pull/lean concepts, the practitioner literature has progressively simplified these terms to the point that serious misunderstandings now exist. In hopes of reducing confusion, we offer general, but precise definitions of pull and lean. Specifically, we argue that pull is essentially a mechanism for limiting WIP, and lean is fundamentally about minimizing the cost of buffering variability.

411 citations


"On the backlog-sequencing decision ..." refers methods in this paper

  • ...It is essentially a pull system (Hopp and Spearman 2004) that uses a so-called Work-In-Process (WIP) limit or cap (WIP-Cap) that is pre-established by management to realise input/output control (Wight 1970; Plossl and Wight 1971)....

    [...]


Journal ArticleDOI
Abstract: The paper reviews ‘classic approaches’ to Production Planning and Control (PPC) such as Kanban, Manufacturing Resource Planning (MRP II) and Theory of Constrains (TOC), and elaborates upon the emergence of techniques such as Workload Control (WLC), Constant Work In Process (CONWIP), Paired cell Overlapping Loops of Cards with Authorization (POLCA) and web- or e-based Supply Chain Management (SCM) solutions. A critical assessment of the approaches from the point of view of various sectors of the Make-To-Order (MTO) Industry is presented. The paper considers factors such as the importance of the customer enquiry stage, company size, degree of customization and shop floor configuration and shows them to play a large role in the applicability of planning and control concepts. The paper heightens the awareness of researchers and practitioners to the PPC options, aids managerial system selection decision-making, and highlights the importance of a clear implementation strategy. WLC emerges as the most effective Job Shop solution; whilst for other configurations there are several alternatives depending on individual company characteristics and objectives. The paper outlines key areas for future research, including the need for empirical research into the use of Workload Control in small and medium sized MTO companies.

326 citations


"On the backlog-sequencing decision ..." refers background or methods in this paper

  • ...…Control – and its card-based variant, Control of Balance by Card Based Navigation (COBACABANA: Land 2009; Thürer, Land, and Stevenson 2014) – is an alternative production planning and control system to ConWIP that was developed for high-variety contexts (Stevenson, Hendry, and Kingsman 2005)....

    [...]

  • ...Fredendall, Ojha, and Wayne Patterson (2010) and Thürer et al. (2015) recently demonstrated the potential for performance improvement from using a backlog-sequencing rule developed by Philipoom, Malhotra, and Jensen (1993) – the capacity slack (CS) rule – in combination with Workload Control order release....

    [...]

  • ...Meanwhile, Thürer et al. (2015) corrected the processing times by dividing the processing time of an operation at a station by the station’s position in a job’s routing....

    [...]


Frequently Asked Questions (2)
Q1. What are the contributions mentioned in the paper "On the backlog-sequencing decision for extending the applicability of conwip to high-variety contexts: an assessment by simulation" ?

This study therefore investigates the potential of the backlog-sequencing decision to improve load balancing in the context of ConWIP, thereby making it suitable for more complex, high-variety environments. Using simulation, the authors demonstrate that: ( i ) the choice of backlog-sequencing rule significantly impacts throughput times and tardiness related performance measures ; and, ( ii ) capacity slack-based sequencing rules achieve significant performance improvements over ‘ classical ’ ConWIP backlog-sequencing rules. This negatively impacts the performance of modified capacity slack-based sequencing rules that were recently shown to be the best choice for Workload Control. 

In fact, a major future research direction may be the exploration of how increased data availability can be used to enhance the performance of simple control systems without jeopardizing their simplicity. This is justified by the broad set of backlog-sequencing and dispatching rules considered, i. e. the authors chose to extend their experimental setting in terms of the number of rules considered rather than the number of environmental variables. Nonetheless, future research could explore the impact of other environmental variables on the relative performance of the backlog-sequencing rules. In response, future research should explore whether only considering the direct load also enhances the performance of Workload Control.