Int. Journal of Business Science and Applied Management, Volume 6, Issue 3, 2011

A comparison of push and pull production controls under

machine breakdown

Joshua Prakash

School of Mechanical Engineering, Universiti Sains Malaysia

(USM), Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia

Telephone: +00604-5996365

Email: chinjengfeng@eng.usm.my

Abstract

Production control for high-mix production remains a complex issue. Common pull system replenishment

generates large works-in-process (WIPs) for each part type, especially under breakdown. This paper attempts to

solve this by presenting a production control that classifies parts into two categories. The performances of three

CONWIP in terms of service level. With high card count, parallel CONWIP generally produces lower

bottleneck utilizations while maintaining a low average flow time per part than shared CONWIP.

Keywords: push system, pull system, CONWIP, machine breakdown, multiple product types

Acknowledgement: The authors wish to acknowledge Universiti Sains Malaysia for the full financial support

through the short term grant no. 6039031 and the Postgraduate Fellowship Scheme.

1 INTRODUCTION

In recent years, the use of pull systems has become increasingly prevalent in many industries to reduce

WIP and other lean wastes directly or indirectly. There are many forms of pull systems, such as the single-stage

kanban system, which has been thoroughly investigated through mathematical formulations and simulation

studies. There are also reported industrial case studies of its applications over a long period. In comparison,

constant WIP (CONWIP) systems are relatively less explored, particularly its behavioral peculiarity against

emergent production challenges. One challenge faced by the CONWIP system is the production synchronization

of multiple part types (Spearman, 1990). Ryan and Vorasayan (2005) proposed a method of allocating cards to

each part type present in a part family, as opposed to sharing cards among part types. The simulation study

revealed superior performance in terms of service level, but several flaws are overlooked. Finished goods

superiority of the CONWIP system in the presence of breakdown is clear, this fact may not hold true if several

part types are considered.

This paper compares the performance of one push system and two pull systems in a D/D/1/∞/∞ queue of a

serial production line, with breakdown following a distribution and no setup. The two pull systems described are

the shared and parallel CONWIP systems. The paper is organized as follows. Sections 2 provide literature a

review on the push and pull systems, CONWIP system, and machine breakdown. Section 3 and 4 describes the

methodology and model construction of the study respectively. Section 5 highlights the results obtained from

the study. Section 6 provides the analysis of the results. Section 7 concludes the paper.

2 LITERATURE REVIEW

Production control mechanisms can be divided into two categories: push system and pull system. In the

accrued. Several production planning tools associated with the push system are material requirement planning

(MRP) and manufacturing resource planning (MRP II).

Around the same time that MRP and MRP II emerged, Japan was facing ordeals in developing its

automotive industry. Lean manufacturing emerged as a solution, where complete waste elimination is the goal

of the industry. One core principle of lean manufacturing is just-in-time. The pull system stems from this

principle. A study conducted in the 1990s revealed the superiority of the pull over the push system. American

and European industries were thus drawn to the pull system and its potential benefits. Womack, Jones, and Roos

(1990) provided an excellent review on this shift in production control preference. Several notable benefits

realized with the pull system include usage of actual demand in production and consideration of capacity

utilization in setting WIP levels. In the long run, inventory costs are lowered.

(1998) compiled the notable benefits of the CONWIP system from previous studies.

One common ground in many studies on the CONWIP system is the determination of system parameter

value(s) for a desired performance level. Hopp and Roof (1998) established a method known as statistical

throughput control used to set WIP levels in production and assembly lines employing the CONWIP system

based on a desired throughput level. Cao and Chen (2005) developed and solved a mathematical model to obtain

optimal part assignment, part sequence, and lot size in production and assembly lines employing the CONWIP

system. The performance measures used are setup time and work load balance. Framinan, Ruiz-Usano, and

Leisten (2000) examined the effect of different dispatch rules in a five-station CONWIP system flow shop via

simulation. The performance measures used are flow time, WIP level, and throughput.

CONWIP system and their performances are compared. Khojasteh-Ghamari (2009) compared the kanban and

CONWIP systems applied in an assembly system in terms of the WIP level and throughput. Framinan,

Gonzalez, and Ruiz-Usano (2003) have highlighted other notable comparisons.

Most studies on the CONWIP system are solely from the academic perspective. This fact does not diminish

the effectiveness of the CONWIP system because there are studies that discuss its industrial application,

although few and far between. Spearman, Hopp, and Woodruff (1989) reported a CONWIP system in the

process of installation by a large computer manufacturer. The background of the concept introduced in the

factory and specific implementation issues such as throughput monitoring is discussed. Gilland (2002)

introduced the CONWIP system in an Intel microprocessor factory. Several wafer release policies in cases with

single and multiple bottlenecks are further analyzed via simulation.

revealed the necessary level of WIP for each part type under varying breakdown and repair rates. Both these

studies reveal the importance of addressing the influence of multiple part types in production; failure to do so

may accrue unnecessary costs (Gharbi and Kenne, 2003).

Smalley (2009) proposed a method where part types are grouped based on their demand pattern. Each

group operates via a common production control mechanism, but differs in the setting. Despite consideration of

additional factors is required, the primary aim is to deal with environments where demand fluctuates. Although

the proposed method describes works in an environment employing the kanban system, the method can be

adopted in the CONWIP system. Most recently, Prakash, Chong, Mustafa, and Chin (2011) described the

workings of such a system (termed parallel CONWIP system) in a three-product shared-machine facility with

admittance of reworked parts. The results reveal the superiority of the said system. Some studies involve

Many studies on the performance of shop floor assume 100% reliability in machines, which is far from

reality. In fact, failure is a characteristic possessed by all entities on the shop floor related to production.

Machine failure behavior is typically characterized by a bathtub curve (Lewis and Chen, 1994). With this

behavior, machine failure, or rather, machine breakdown, may be represented by a distribution. Two parameters

commonly addressed are the mean-time-to-failure (MTTF) and mean-time-to-repair (MTTR).

The impact of machine breakdown has been addressed in both push (Chung, 2003; Wazed, Ahmed, Yusoff,

2010) and CONWIP (Graves and Milne, 1997; Ozbayrak Cagil, and Kubat, 2004) systems. Several alternatives

for dealing with machine breakdown have been proposed. Abboud (2001) compiled these alternatives from

previous studies. Production in excess of demand is the essence of many of these alternatives. The difference

between them lies in the point at which production stops and resumes. However, the main goal of any method

parameter at a time. The models are verified in two methods. First, the models are made deterministic and the

results obtained from the initial simulation run are compared with ones generated from manual calculation. The

models are fine-tuned until both results are matched. Second, the performances of the models are observed and

i = 1, 2, 3….

T

= warm-up period

Let f

=0, f

=0, g

=0 and g

=0 at the start of each simulation run

if t ≥ T

f

HR

= f

HR + 1

else

g

HR + 1

endif

if c

≤ d

,

f

= f

LR service level =

Average flow time per part =

4 RESULTS

reveal negligible effect on the performance measures.

With regards to SC-FIFO, all performance measure in the study reveals no significant changes with respect

to HRLR. This is due to the absence of assignment of cards between HR and LR orders. Likewise, within the

context of interaction, interactions of HRLR with any remaining factors poses no significant effect on the

performance measures of this study.

In P-FIFO the performance measures of this study are independent of changes in HRLR. This is consistent

with a push system behavior where the only determinant is the lot size, and manipulation of this variable brings

about significant changes in the aforementioned performance measures. However, the analysis also reveals that

changes in BC bring no significant effect to HR and LR service level as well as average flow time per part. This

is due to the sufficiently high WIP levels within the system such that the effect of breakdown is suppressed. As

for interaction between factors, only changes in both BC and LS impose significant effect in the performance

measures of this study.

Figure 2: HR service level vs. HRLR for SC-FIFO, PC-FIFO, PC-HL, and PC-LH.

SC-FIFO overlaps with PC-LH

The LR service level of PC-LH is as good as that of SC-FIFO (Figure 3), maintaining 100% HR service

level irrespective of HRLR. For PC-FIFO, although the HR service level is adequately high, it is still lower than

that of SC-FIFO, and exhibits an increase with HRLR. PC-HL has the lowest HR service level among all

systems, and exhibits a minimum at approximately HRLR=0.45. LR service level reflects a similar behavior to

that of HR service level.

FIFO is slightly lower than SC-FIFO. With the HL and LH dispatch rules, throughput is suppressed even

further. Throughput of all systems shows a decrease with increasing BC.

LH.

PC-HL overlaps with PC-LH

The average flow time per part of SC-FIFO is the highest compared to remaining PC variants (Figure 5).

PC-FIFO is slightly lower than this and the presence of HL and LH loading rules suppress the average flow time

per part even further. The average flow time per part of all systems show an increase with increase in total

number of CONWIP cards.

Figure 6: Bottleneck utilization vs. BC for SC-FIFO, PC-FIFO, PC-HL, and PC-LH.

5 DISCUSSION

In essence, between the SC and P systems, a larger proportion of parts in the former are able to meet the

due date set while maintaining a low WIP level. This finding is consistent with that of JodlBauer and Huber

(2008). The primary reason for this behavior is the limit placed on WIP in SC-FIFO. In P-FIFO, WIP

accumulates at the immediate upstream buffer of the bottleneck; with higher breakdown rate, WIP accumulation

is higher. This finding highlights one benefit of the SC over the P systems; in the presence of breakdown, the

admittance of fresh parts is suppressed due to the absence of free cards. Although machine breakdown is in fact

an unpredictable event, the occurrence of breakdown naturally controls fresh parts from being admitted into the

line, in accordance with the number of cards present. The absence of this control mechanism in the event of

breakdown, as in the P system, affects the service level.

is sufficient to suppress the effect of increased HR volume. However, beyond this point, the HR service level

increases, and as at any given instance, LR can be absent from a queue. Although PC-FIFO does not perform as

well as SC-FIFO in terms of the service level, the practical simplicity of the categorical dispatch rules are

sufficient to elevate the service level.

The LR service levels in decreasing order are SC-FIFO and PC-LH, PC-FIFO and P-FIFO, and PC-HL.

Between the SC and PC systems, the former maintains its behavior as before, whereas the behavior of PC

variants is the inverse. In Figure 3, the minimum point in the LR service level of PC-HL is explained as follows.

At HRLR values lower than that at the minimum point, the effect of the HL dispatch rule coupled with increased

HR volume has a negative effect on the LR service level. Beyond this point, although LR orders have the two

said factors working against it, the LR service level increases. With a smaller volume of LR, there is lesser

chance for a given LR order not to meet the specified due date.

The throughputs in decreasing order are P-FIFO, SC-FIFO, PC-FIFO, and PC-HL and PC-LH. Figure 4

shows that the throughput of P-FIFO remains highest as parts are constantly admitted into the line irrespective

of downstream needs. P-FIFO, despite producing more parts, still yields a lower service level. Between the SC

and PC systems, the WIP levels are limited by the number of cards present, hence the lower throughput.

Throughputs of PC variants are lower than those of SC-FIFO, albeit only a small difference. This slight

and PC-LH. Any given part in P-FIFO spends a large proportion of time waiting for processing to begin. A

lower average flow time per part corresponds to a larger lot size, and a larger lot size comes with increased WIP

level. On the other hand, SC-FIFO exhibits a constant average flow time per part. With a larger lot size, each

batch spends a longer time in processing, thereby delaying the cards from being freed and limiting the net WIP

in the line. Lesser WIP queues indicate lesser waiting time.

Between the SC and PC systems, the summation KCHR and KCLR in PC variants at any given instance

corresponds to the equivalent KC in SC-FIFO. In Figure 5, due to the absence of allocation between HR and LR

in SC-FIFO, both categories have equal chances of obtaining a card. However, in PC-FIFO, a larger HRLR

increases the average flow time per part, as more orders need to wait for HR cards in the line to be freed. This

phenomenon causes the range of average flow time per part in PC-FIFO to be larger than that in SC-FIFO. This