System Dynamics & Factory Physics: Continuous Improvement on Lot Size

Abstract Number: 008-0127
Combining System Dynamics And Factory Physics Approach To
Study The Effect Of Continuous Improvement On Lot Size – Cycle
Time Relationships
Moacir Godinho Filho
Universidade Federal de São Carlos- Departamento de Engenharia de Produção
Via Washington Luiz, km 235 - Caixa Postal 676 - São Carlos-SP – Brazil 13.565-905
[email protected]
Reha Uzsoy
Edward P. Fitts Department of Industrial and Systems Engineering
North Carolina State University- Campus Box 7906 - Raleigh, NC 27695-7906
[email protected]
POMS 19th Annual Conference
La Jolla, California, U.S.A.
May 9 to May 12, 2008
Abstract
We compare the effect of continuous improvements in six system parameters (arrival and
process variability, defect rate, time to failure and repair and setup time) on the relationship
between lot size and cycle time in a single stage multi-product environment. We use System
Dynamics (SD) to capture the evolution of the system over time and the Factory Physics
approach to represent the relationships between system parameters and cycle time. Some results
are: i) The positive effect of improvement on setup time and on defect rate increases as lot sizes
are shorter; ii) improvements on process variability achieves the best result when lot sizes are
large and improvement on setup time achieves the best result when lot sizes are small; iii)
simultaneous improvement in all variables outperforms large improvements in any individual
variable, except with very small lot sizes
Keywords: lot size, cycle time, system dynamics, factory physics, continuous improvement
1. Introduction
Day-to-day operations decisions, such as lot sizing, can have major strategic implications
for the firm Buffa (1984). An extensive literature on lot sizing problems exists, most of which
seeks to find an optimal lot size based on a cost model (called EOQ-Economic Order Quantity or
EPQ – Economic Production Quantity). For reviews of the lot sizing literature see Drexl and
Kimms (1997) and Karimi et al. (2003). Despite its benefits, the EOQ model has received a lot
of criticism in the literature, such as failing to consider several effects and costs when it
calculates the order quantity of a part.
According to Kuik and Tielemans (2004) criticism of cost accounting methods for
production planning led to a growing interest on physical measures of manufacturing
performance. One of these measures is manufacturing cycle time (also known as lead time or
flow time), defined as the mean time required for a part to complete all its processing. TBC (also
called Responsive Manufacturing by Kritchanchai and MacCarthy (1998) was initially proposed
by Stalk (1988) and focuses on cycle time reduction as a primary manufacturing goal. According
to Jayaram et al (1999) many companies pursuing Time-based strategies have experienced big
improvements in various dimensions of time-related performance. Hum and Sim (1996) presents
a review of the TBC literature.
Despite this interest in cycle time reduction, Treville et al.(2004) point out that much of
the literature on cycle time reduction has been largely anecdotal and exploratory. Some
exceptions are: i) the work of Hopp and Spearman (2001), who compiled a set of mathematical
principles determining cycle time – based on queueing theory – which they refer to as factory
physics; and, ii) the work of Suri (1998) on cycle time reduction with his manufacturing strategy
(also based on queueing theory) called Quick Response Manufacturing.
Both strategies (Factory Physics and Quick Response Manufacturing) present a set of
relationships between variables on the shop floor, aiming to increase the manager’s
understanding of manufacturing dynamics. One of these is the relationship between lot size and
average cycle time, which is studied in this paper. According to Vaughan (2006) although the
general relationship is widely recognized, only a few authors have addressed the relationship
between lot size and job queueing characteristics (cycle time). Karmarkar et al. (1985b) first
introduced the convex relationship between lot size and average cycle time. This relationship is
derived from Queueing Theory, specifically the Kingman approximation of the expected time in
system for the G/G/1 queue, Hopp and Spearman (2001). Figure 1 ilustrates the relationship.
Lambrecht and Vandaele (1996) describe this relationship: “Large lot sizes will cause long cycle
times (the batching effect), as the lot size gets smaller the cycle time will decrease but once a
minimal lot size is reached a further reduction of the lot size will cause high traffic intensities
resulting in longer cycle times (the saturation effect)”.
3000
Cycle Time (hours)
2500
2000
1500
1000
500
0
0
100
200
300
400
500
600
Lot size (pieces)
Figure 1: Relationship between lot size and cycle time
700
A number of other papers have used queueing models and simulationto study the
relationship between lot size decisions and cycle time. Karmarkar, Kekre et al. (1992) present
heuristics for minimizing average queue time. Rao (1992) discusses alternative queuing models
in relation to lot sizes and cycle time. Lambrecht and Vandaele (1996) develop approximations
for the expectation and variance of the cycle time under the assumption of individual arrival and
departure processes. Kuik and Tielemans (2004) develop approximations for the optimal lot size
that minimizes the cycle time, focusing mainly on situations where the utilization is low. Kenyon
et al. (2005) use simulation to evaluate the impact of lot sizing decisions on cycle time in a
semiconductor company. Vaughan (2006) use queueing relationships to model the effects of lot
size on cycle time. Other papers relating lot size and cycle times are Kekre (1987), Hafner (1991;
Karmarkar et al. (1992; Lambrecht and Vandaele (1996), and Choi and Enns (2004).
Although these studies provide a basis for understanding the relationship between lot
sizes and cycle times, there is no literature showing the impact of continuous improvement (CI)
efforts on this relationship. This is despite the repeated observation that reduction of lot sizes and
the resulting reduction of cycle times is a critical component of lean manufacturing and the
Toyota Production System (e.g., Liker (2004). This paper examines the effect of six continuous
improvement programs on the lot size-cycle time relationship in a multi-product, single-machine
environment using a combination of system dynamics and factory physics.
The remainder of this paper is organized as follows: the next section gives a short
literature review on the two modeling approaches used in this paper (system dynamics and
factory physics) and also about CI; section 3 presents the model developed and also the
experimental design; section 4 shows the results of the experiments and section 5 presents some
conclusions.
2. Literature review
2.1 System Dynamics
System Dynamics models capture the causal relationships and feedbacks among state and
decision variables in the system. System Dynamics has been applied for designing models to
understand and solve problems in a variety of areas such as industrial planning (Forrester
(1962)), etc); economy (Chen and Jan (2005), Kumar and Yamaoka (2007), etc); among other.
However, there is a lack of SD applications to manufacturing systems, even with evidence that
this technique is suitable for industrial modeling. According to Baines and Harrison (1999) the
computer simulation of manufacturing systems is commonly carried out using Discrete Event
Simulation (DES). These authors conclude that manufacturing system modeling represent a
missed opportunity for SD modeling. This is also the opinion of Lin et al.(1998), which propose
a framework to help industrial managers to apply SD to manufacturing system modeling. This is
one of our motivations for the work in this paper. Recent advances in interactive modeling, tools
for representation of feedback structure, and simulation software make SD models much more
accessible to practitioners than they have been in the past (Sterman (2000)).
2.2 Factory Physics
The Factory Physics approach was created by Hopp and Spearman (2001) to provide
managers with a science of manufacturing. It is based on a set of mathematical principles derived
from Queueing Theory. This approach has, according to Hopp and Spearman (2001), three
properties: it is quantitative, simple and intuitive; so it provides managers with valuable insights.
The basic approach consists of a set of equations that relate the long-run steady state means and
variances of critical performance measures such as cycle time and work in process inventory
(WIP) levels to the mean and variance of system parameters such as time between failures, setup
times and processing times.
2.3 Continuous Improvement (CI)
There is an extensive literature examining various aspects of continuous improvement
efforts in industry. Caffyn (1999) defines continuous improvement as “… a mass involvement in
making relatively small changes which are directed towards organizational goals on an ongoing
basis.” Continuous improvement has been recognized for many years as a major source of
competitive advantage, and is inherent in many recent management movements such as the
Theory of Constraints (Goldratt and Fox (1986)), Six Sigma (e.g, Pande et al. (2000)) and the
Toyota Production System. Inability to effectively implement continuous improvement programs
is seen by many scholars and practitioners as one of the reasons why Western firms have not
fully benefited from Japanese management concepts (e.g., Berger (1997)). Savolainen (1999)
points out that CI is a complex process that cannot be achieved overnight, but involves
considerable learning and fine tuning of the mechanisms used (Bessant and Francis (1999)).
A number of different aspects of CI have been studied in the literature. However, despite
its importance, there are still significant gaps in our understanding of this important area of
manufacturing management. In particular, there appears to be a lack of quantitative studies
examining how different CI approaches affect manufacturing performance over time. This paper
is an attempt to fill this gap by building a quantitative model of the effects of six different CI
programs, and then using a SD simulation to examine how these improvements affect the lot size
x cycle time relationship over time.
3. Modeling and Analysis
The use of the factory physics concepts in a system dynamics model may, at first glance,
appear to be somewhat contradictory. The factory physics approach as presented in Chapters 8
and 9 of Hopp and Spearman), is based on long-run steady state analysis of the production
system, generally derived using the methods of queueing analysis. System dynamics, on the
other hand, usually emphasizes the dynamic behavior of complex systems that are not in steady
state. However, for our objective in this paper the factory physics equations are unique in
providing a systematic mathematical model linking the mean and variance of key system
parameters such as setup and repair times to key performance measure such as cycle time and
utilization. In order to use these equations as the basis for a system dynamics model, we assume
that the time increments that form the basis of the system dynamics model are quite long,
corresponding to periods of the order of several months. This is a reasonable assumption in our
context, since it generally takes some time to identify opportunities for improvements,
implement the necessary changes and obtain the results. We thus assume that within each time
period the queue representing the manufacturing system will be in steady state, allowing us to
use the factory physics equations to describe the behavior of the system. The assumption of long
time periods also allows us to neglect the transient behavior at the boundaries between time
periods.
Our basic approach then is to model the performance of the system over an extended time
horizon of several years using time increments of the order of several months. Continuous
improvement policies are modeled as a reduction in the mean or variance of the parameters
studied (arrival variability; process variability; quality; time to failure; repair time, and; setup
time) obtained in each period. In each period, the new parameter values are calculated based on
the improvements implemented in the previous period, and the factory physics equations are
used to propagate the effects of these improvements to system performance measures, and then
the operating curve can be drawn. Thus we obtain the trajectory of improvements in operation
curves due to the improvements in the parameters considered. We assume a completely
deterministic model of the effects of continuous improvement on cycle time for simplicity,
following the suggestion of Sterman (2000) that a deterministic approach is generally sufficient
to capture the principal relationships of interest. Note that the effects of randomness in the
operation of the system itself are captured by the variances used in the factory physics equations,
and our primary performance measure is the expected cycle time of the system in each period.
Surprisingly, given the extensive discussion of continuous improvement and cycle time
reduction in the literature and industry, there seems to be very little industrial data available on
the rates of improvement realized over time in different industries, which makes it difficult to
calibrate models of this type.
3.1 The model
We consider a manufacturing system modeled as a single server with arbitrary
interarrival and processing time distributions, which we shall represent as a G/G/1 queue. We
assume the natural processing time, the time required to process a job without any detractors
other than the variability natural to the production process, has a mean of t0 and a standard
deviation of σ0. We shall denote the mean effective time to process one good part, which is the
natural processing time modified by the impacts of disruptions such as setups and machine
failures, as te, and its coefficient of variation as ce. We assume that work arrives at this station in
lots consisting of L parts on average, and the interarrival time between lots has mean ta and
coefficient of variation ca. The arrival rate λ of lots is the inverse of time between arrivals,
giving λ = 1/ta. If we denote the mean annual demand by D, since the system must be in steady
state to avoid unbounded accumulation of jobs in the queue, the mean arrival rate to the system
must equal the mean demand rate, implying ta = LH/D, where H denotes the total number of
hours worked in a year. The mean time to process a lot of L parts is then given by Lte, and the
mean utilization of the server by:
u=
Lte
ta
=
Dte
H
(1)
The other primary performance measure of interest in this study is the mean cycle time.
For the G/G/1 queue, no exact analytical expression exists, but the following approximation has
been found to work well and is recommended by Hopp and Spearman (2001):
CT =
(ca2 + ce2 )
2
(
u
)Lt + Lte
1− u e
(2)
where LTe is the mean time to process one lot.
The effective time to make one piece is constructed from the natural processing time by
adding in first the effects of preemptive disruptions, in our case machine failures, then the effects
of nonpreemptive outages, in our case setups; and finally the effect of defective items. Both the
mean and the variances of the effective processing times must be calculated, as reflected in the
model. Thus we first calculate the mean and variance of the intermediate effective processing
time with machine failures, which we shall denote as tef. Following Hopp and Spearman’s
treatment, we shall assume that the time between failures is exponentially distributed with mean
mf, and that the time to repair has mean mr and variance σ2r. Then the mean availability of the
server is given by A = mf/(mf + mr), yielding tef = t0/A, and
( σ ef )2 =
σ 02
A2
+
(mr2 + σ r2 )(1− A )t0
Amr
(3)
We now incorporate the effects of setups, assuming, as in (Hopp and Spearman (2001),
that a setup is equally likely after any part is processed, with expected number of parts between
setups equal to the specified lot size L. The mean setup time is denoted by ts, and its variance by
σs2. We thus obtain the mean of effective processing time with both nonpreemptive and
preemptive outages (denoted by teo)as teo = tef + ts/L. Its variance is given by:
(4)
Finally, incorporating the effect of defective items, we have the overall mean of the
effective processing time te, given by te = teo /(1-p), where p denotes the proportion of defective
items. The overall variance of the effective processing time is given by:
(5)
Figure 2 shows all of these relationships.
Number of pieces on
queue
arrival rate
throughput
TIME WORKED
DURING THE YEAR
ANNUAL
DEMAND
production rate
Utilization
Lot Size
Overall mean of
effective processing
time
Coefficient of variation for
effective processing time
Set up Time with
improvement
Arrival coefficient of
variation with
improvement
Mean of effective processing
time with nonpreemptive and
preemptive outages
Total WIP
Queue Time
Variance of set up time
with improvement
Variance of of effective processing
time with nonpreemptive and
preemptive outages
Variance of natural
process time with
improvement
Overall variance of
effective processing time
NATURAL
PROCESS TIME
Cycle Time
Variance of effective processing
with preemptive outage
(machine failures)
Defect Rate with
improvement
Availability
Variance of repair time
with improvement
Mean time to failure
with improvement
Mean repair time with
improvement
Figure 2: Main part of the SD model developed in this paper
Since our objective is this paper is to examine the effects of continuous improvement in
six parameters on the cycle time of the system over time, we need a mechanism to model
continuous improvements. We use an exponential model of improvement, where the value of a
parameter A at time t is given by:
A(t) = (A0 ‐ G)e‐t/τ
(6)
where A0 denotes the initial value of the parameter, and G the minimum level to which it can be
reduced. The parameter τ represents the amount of time it takes for the improvement to take
place, representing in our case the difficulty of improving the parameter in question. Figure 2
shows the SD structures used to model improvement on mean setup time. This structure is linked
into the variable setup time with improvement in Figure 1. Similar structures are used to model
the improvements on the other variables studied on this paper (arrival variability, process
variability (setup time variability, repair time variability and natural process time variability),
quality, mean time to failure and repair time. These structures are linked to the following
variables, respectively: arrival coefficient of variation with improvement, variance of setup time
with improvement, variance of repair time with improvement, variance of natural process time
with improvement, defect rate with improvement, mean time to failure with improvement and
repair time with improvement.
Improvement
on set up time
Improvement rate on
set up time
ADJUSTMENT TIME FOR
SET UP TIME
IMPROVEMENT
+
B2
- Error on set up time
improvement
VARIANCE OF
SET UP TIME
GOAL REGARDING SET
UP TIME
IMPROVEMENT
Set up Time with
improvement
Figure 3: SD Structure of improvement on setup time
3.2 Parameters of the model
We will vary individual parameters in different experiments to examine their effect on
cycle times and utilization. The basic time period in the system dynamics model is assumed to be
three months, or 12 weeks. This is a reasonable time incremet or the purposes of this paper, since
it is likely to require several months to develop and implement the improvements needed to
make a significant reduction in repair or setup times. We simulate the operation of the system
over a period of ten years, or 40 quarters. Since our objective is to study the effects of
continuous improvement in repair and setup times, the annual demand is held constant at D =
11520 parts per year. We assume an initial lot size of 200 parts, and that the plant operates a total
of H = 1920 hours per year. The interarrival times are assumed to be exponentially distributed (ca
= 1), as is the natural processing time per part, with t0 = 6 minutes and c0 = 1. At the start of the
simulation, the mean time between failures mf = 9600 minutes, the mean time to repair is mr =
480 minutes, and the mean setup time is ts = 180 minutes. Also, the parameter τ of the
improvement process was chosen to provide a half life for the exponential decay of 1 year. The
proportion of defective items p = 5%.
We also vary the lot size in order to examine the effect of lot sizes on cycle time. This is
done for all improvement policies tested: (i) No improvement; (ii) 50% improvement in arrival
variability; (iii) 50% improvement in process variability (natural process variability, repair time
variability and setup time variability); (iv) 50% reduction on defect rate; (v) 50% increase in
time between failures; (vi) 50% improvement in repair time; (vii) 50% improvement on setup
time; (viii) 5% improvement in all variables; (ix) 10% improvement in all variables; (x) %
improvement in all variables; (xi) 20% improvement in all variables.
4. Experimental Results
Figure 4 shows the effect on mean cycle time of all 50% improvements over the time
period (cases (i) to (vii)) for a lot size of 200 parts. Figures similar to this are also draw for the
other lot size values tested (600, 400, 170, 150, 130, 100, 80, 70, 60, 40, 30). From these figures,
lot size x cycle time curves are drawn for each improvement policy. This is done by getting the
cycle time and after these curves become stable. The resulting lot size x cycle time curves for the
large improvement programs are discussed in Section 4.1. The same procedure is performed for
the small improvement program and reported in Section 4.2).
Graph for Cycle Time
600
500
400
300
200
0
30
60
Time (months)
90
Improvement in Process variability
Improvement in Arrival cv
Improvement in Time to failure
Improvement in defect rate
Improvement in repair time
Improvement in set up time
No improvement
120
hour/batch
hour/batch
hour/batch
hour/batch
hour/batch
hour/batch
hour/batch
Figure 4: Behavior (regarding cycle time) of all 50% improvements over time for a lot size of 200 parts
4.1 Effect of large improvements in just one variable on lot size x cycle time relationship
Figure 5 presents an overview of the effect of each of the six 50% improvement policies
on the mean cycle time (cases (ii) to (vii)). In this figure it can be seen that:
- 50% improvement in arrival cv has slight effect on cycle time reduction for given lot
size. This effect remains small when lot sizes are decreased;
- 50% improvement in defect rate, repair time, time to failure, setup time and process
variability shift the lot size x cycle time curve down and left, reducing the optimal lot size for
cycle time. Hence these improvements help the production system to reduce cycle time over the
no improvement case (for the same lot size used);
- The cycle time benefit of 50% improvement in setup time and in defect rate increase as
lot sizes are reduced. The cycle time reduction from 50% improvement in repair time and time to
failure increase at extreme values of the lot sizes (very small or very large lot sizes are used);
- 50% improvement in setup time achieves the best cycle time reduction for small lot
sizes. Improvement in setup time is the only improvement that allows production system to set
really small lot sizes (e.g. 30 or 40 parts);
- 50% improvement in process variability achieves the best cycle time reduction for large
lot sizes .
4000
3500
3000
Lead Time (hours)
No improvement
Improvement in set up
2500
Improvement in defect rate
Improvement in arrival cv
2000
Improvement in repair time
Improvement in time to failure
1500
Improvement in process variability
1000
500
0
0
100
200
300
400
500
600
700
Lot Size (pieces)
Figure 5: Overview of the effect of all 50% improvements on lot size x cycle time curve
4.2 Effect of small improvements in several variables on lot size x cycle time relationship
Figure 6 presents a comparison between the two large improvements that achieves the
best results on the last section and a small (5%, 10%, 15% or at most 20%) improvement in all
variables simultaneously. In this figure a 15% simultaneous improvement in all variables
outperforms all the large individual improvements, except for very small lot sizes are used (large
improvements on setup time achieves the best result in this case).
3000
2500
5% improvement in all variables
Lead Time (hours)
2000
10% improvement in all variables
15% improvement in all variables
1500
20% improvement in all variables
1000
50% Improvement in set up
50% improvement in process
variability
500
0
0
100
200
300
400
500
600
700
Lot Size (pieces)
Figure 6: Comparison between the large improvement on setup and process variabilities and small combined
improvements on all variables
5. Conclusions
We combine System Dynamics (SD) and Factory Physics approaches to study the effect
of six continuous improvement programs (arrival variability, process variability (natural process
time variability, repair time variability and setup time variability), quality (defect rate), time to
failure, repair time and setup time) on lot size -cycle time relationship in a multi-product, singlemachine environment. Two sets of experiments were performed: a) A large (50%) improvement
in each parameter separately, as might be obtained by a significant one-time investment; b) a
small improvement in all parameters simultaneously. Some insights from this study can be
summarized as follows:
- All 50% improvements studied, except arrival cv, shift the lot size - cycle time curve
down and to the left, reducing the optimal lot size for cycle time. Thus these improvements help
the production system to reduce cycle time relative to the no improvements case (for the same lot
size used). These results provide support for the CI literature, which argues that the need to
continuously improve became imperative for companies nowadays. 50% improvement on arrival
cv showed just a little effect on cycle time reduction. This effect is still small when lot sizes are
decreased. This result shows that efforts for reducing the variability of the arrival orders does not
have a lot of effect on cycle time reduction; so it is possible to reduce cycle time by means of the
implementation of other CI programs even in an environment when arrival cv is high. Another
conclusion that can be drawn from these results is that a pull production control system (like
kanban), which is advocated in the Lean Manufacturing literature as an important means to
achieve flow standardization (and therefore reducing the arrival cv), is not vital to achieving
cycle time reduction. This conclusion supports the Quick Response Manufacturing (QRM)
theory, proposed by Suri (1998), which claims that kanban is not the best production control
system for cycle time reduction.
- The reduction in cycle time by 50% improvement in setup time and defect probability
increase as lot sizes decrease. These results provide support for all the modern manufacturing
management theories, such as Lean Manufacturing and QRM, which advocates the need for lot
size reduction. Despite this result it is interesting that the convex relationship between lot size
and cycle time implies that reducing the lot size (without knowing exactly the shape of this
convex relationship) by itself does not guarantee cycle time reduction. Huge reduction of lot
sizes (in our case for 40 or 50 pieces), even with some kind of improvement (for example
variability reduction, improvement on quality or time to failure) can have worse cycle time
performance than a more smooth reduction on lot size (in our case for about 150 pieces) and no
continuous improvement program. This result supports the Quick Response Manufacturing
(QRM) theory, which claims that a lot size of one piece, contrary to what is advocated by Lean
Manufacturing, actually contributes to increased cycle time. Thus it is important to know exactly
the relationship between lot size and cycle time before deciding the amount of lot size reduction
to be performed;
- The cycle time reduction from 50% improvement in repair time and time to failure
increases as lot sizes take extreme values. Thus when a firm sets its lot size for a value near the
optimal lot size for cycle time (the minimal value on the lot size x cycle time curve), the need for
improvement programs in machine availability decreases.
- Setup time reduction achieves the best cycle time reduction when lot sizes are small.
Therefore, when improvement in setup time is performed, the amount of lot size reduction can be
greater (in our case for 30 or 40 parts). This result supports the literature which advocates the
need for setup reduction, like those achieved by the SMED (Single Minute Exchange of Die)
system (Shingo (1986));
- Improvement in process variability achieves the best cycle time reduction when lot sizes
are large. This implies that efforts to reduce process variability can be an alternative way to
reduce cycle times in companies where the process itself prevents the use of small lot sizes.
- Investing in small, combined improvements in all shop floor variables yields better
cycle time reduction than a single large, high-investment improvement. 15% simultaneous
improvement in all variables outperforms all the large individual improvements, except where
very small lot sizes are used (large improvements on setup time achieves the best result in this
case). These results provide a lot of support for Lean Manufacturing practice, which believes in
small continuous improvements in all areas and parameters of the company.
Finally, we can say that the SD/factory physics combined model proposed in this paper
can have a practical use for companies to simulate the impact of alternative continuous
improvement programs on manufacturing performance measures. The natural extension of this
paper is to evaluate the result of such improvements for other performance measures, such as
Work In Process and Utilization, and to enlarge the model to encompass more workstations.
6. References
Baines, T. S. and D. K. Harrison (1999). "An Opportunity For System Dynamics In
Manufacturing System Modeling." Production Planning and Control 10: 542-552.
Berger, A. (1997). "Continuous Improvement And Kaizen: Standartization And Organizational
Designs." Integrated Manufacturing Systems 8(2): 110-117.
Bessant, J. and D. Francis (1999). "Developing Strategic Continuous Improvement Capability. ."
International Journal of Operations & Production Management 19(11): 1106-1119.
Buffa, E. S. (1984). Meeting the Competitive Challenge. Homewood - IL, Irwin.
Caffyn, S. (1999). "Development Of A Continuous Improvement Self-Assessment Tool."
International Journal of Operations & Production Management 19(11): 1138-1153.
Chen, J. H. and T. S. Jan (2005). "A System Dynamics Model Of The Semiconductor Industry
Development In Taiwan." Journal of the Operational Research Society 56(10): 1141-1150.
Choi, S. and S. T. Enns (2004). "Multi-product capacity-constrained lot sizing with economic
objectives." International Journal of Production Economics 91: 47-62.
Drexl, A. and A. Kimms (1997). "Lot Sizing and Scheduling - Survey and Extensions."
European Journal of Operational Research 99: 221-235.
Forrester, J. W. (1962). Industrial Dynamics. Cambridge, MA, MIT Press.
Goldratt, E. and R. E. Fox (1986). The Race, North River Press.
Hafner, H. (1991). "Lot sizing and throughput times in a job shop." International Journal of
Production Economics 23: 111-116.
Hopp, W. and M. L. Spearman (2001). Factory Physics. Boston, Irwin.
Hum, S. and H. Sim (1996). "Time-Based Competition: Literature Review And Implications For
Modeling." International Journal of Operations & Production Management 16(1): 75-90.
Karimi, B., et al. (2003). "The capacitated lot sizing problem: A review of models and
algorithms." Omega - International Journal of Management Science 31: 365-378.
Karmarkar, U. S., et al. (1992). "Multi-Item Batching Heuristics for Minimization of Queues."
European Journal of Operational Research 58: 99-111.
Karmarkar, U. S., et al. (1985b). "Lot Sizing and Lead Time Performance in a Manufacturing
Cell." Interfaces 15(2): 1-9.
Kekre, S. (1987). "Performance of a Manufacturing Cell with Increased Product Mix." IIE
Transactions 19(3): 329-339.
Kenyon, G., et al. (2005). "The impact oflot-sizing on net profits and cycle times in the n-job, mmachine job shop with both discrete and batch processing." International Journal of Production
Economics 97: 263-278.
Kritchanchai, D. and B. L. MacCarthy (1998). Responsiveness and strategy in manufacturing.
Proceedings of the workshop Responsiveness in Manufacturing.
Kuik, R. and T. F. J. Tielemans (2004). "Expected time in system analysis of a single-machine
multi-item processing center." European Journal of Operational Research 156: 287-304.
Kumar, S. and T. Yamaoka (2007). "System Dynamics Study Of The Japanese Automotive
Industry Closed Loop Supply Chain." Journal of Manufacturing Technology Management 18(2):
115-138.
Lambrecht, M. R. and N. J. Vandaele (1996). "A General Approximation for the Single Product
Lot Sizing Model with Queueing Delays." European Journal of Operational Research 95: 73-88.
Liker, J. (2004). The Toyota Way: 14 Management Principles from the World's Greatest
Manufacturer. New York, McGraw-Hill.
Lin, C., et al. (1998). Generic methodology that aids the application of system dynamics to
manufacturing system modeling. IEE Conference Publication. 457: 344-349.
Pande, P., et al. (2000). The Six Sigma Way: How GE, Motorola and Other Top Companies are
Honing Their Performance. McGraw-Hill, New York.
Rao, S. (1992). "The relationship of work-in-process inventories, manufacturing lead times and
waiting line analysis." International Journal of Production Economics 26: 217-227.
Savolainen, T. I. (1999). "Cycles of continuous improvement: Realizing competitive advantages
through quality " International Journal of Operations & Production Management 19(11): 1203 1222
Shingo, S. (1986). A Revolution in Manufacturing: The SMED System. Cambridge, Productivity
Press.
Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex
World. New York, McGraw-Hill.
Suri, R. (1998). Quick Response Manufacturing: A Companywide Approach to Reducing Lead
Times. Portland, Productivity Press.
Treville, S. D., et al. (2004). "From supply chain to demand chain: the role of lead time reduction
in improving demand chain performance." Journal of Operations Management 21: 613-627.
Vaughan, T. S. (2006). "Lot Size Effects On Process Lead Time, Lead Time Demand, And
Safety Stock. ." International Journal of Production Economics 100: 1-9.