Manufacturing System Planning and Scheduling

Abstract

This case study concerns support for customised solving of a production planning and scheduling problem in the piece-part
medium-sized manufacturing company. To make the best use of an advanced scheduling tool and to find an optimal configuration
of its rules and parameters, modular simulation models of the entire business/production process and production anodising
stage are developed. Planning scenarios intended for optimising business processes in the company and different sequencing
rules to improve processing of production orders are analysed. The improved approach and its benefits in practice are described.

Full text

19
Y. Merkuryev et al. (eds.), Simulation-BasedCaseStudiesinLogistics
© Springer 2009
Abstract This case study concerns support for customised solving of a production
planning and scheduling problem in the piece-part medium-sized manufacturing
company. To make the best use of an advanced scheduling tool and to nd an opti-
mal conguration of its rules and parameters, modular simulation models of the
entire business/production process and production anodising stage are developed.
Planning scenarios intended for optimising business processes in the company and
different sequencing rules to improve processing of production orders are analysed.
The improved approach and its benets in practice are described.
2.1 Introduction
Modern production scheduling tools are very powerful and offer a vast range of
options and parameters for adapting the tool’s behaviour to the requirements of
the real process. However, the more options exist, the more difcult it becomes to
nd the best conguration of the tool in practice. Even experts cannot often pre-
dict the effects of many possibilities. Testing out even a small number of possible
congurations in reality and studying their effects on the real production process
might take months and might severely reduce the overall performance. Hence, such
tests are not feasible in practice. It is much faster, easier, safer and cheaper to test
Galina Merkuryeva
Riga Technical University, Latvia
gm@itl.rtu.lv
Nigel Shires
Preactor International Ltd., UK
nigel.shires@preactor.com
Chapter 2
Manufacturing System Planning
and Scheduling
G. Merkuryeva and N. Shires

G. Merkuryeva and N. Shires20
and optimise a production scheduler using a simulation model than using the real
process [1].
In order to make the best use of an advanced and sophisticated scheduling tool
in the piece-part medium-sized manufacturing company and to nd an optimal
conguration of its rules and parameters, modular simulation models of the entire
business/manufacturing system and production process anodising stage are built
in order to test out the effects of various scheduler congurations [2]. Testing and
optimisation of the scheduling tool conguration is carried out off-line by using
simulation models. The real production process is not disturbed, and the optimal
conguration can be found very quickly and at low cost.
2.2 Problem Formulation
Decorpart, a UK-based medium-sized manufacturer, produces a wide range of
different small pressed aluminium parts in large quantities to a range of other
consumer-focused businesses. Typical applications include spray assemblies for
perfumes and dispenser units for asthma sufferers. The business lies in a highly
competitive sector, and success depends on achieving high efciency and low cost
of manufacturing. Production scheduling is therefore very critical.
In the past, the company had already installed software tools supporting the
scheduling of individual areas of the production process. To improve the overall
company performance, increase its output and reduce the product lead time, they
have planned to implement an automatic Preactor supply chain planning server – an
overall scheduling system coordinating all local business and production areas. In
order to deliver the best possible solution, the supplier of the scheduling tool, Pre-
actor International (http://www.preactor.com) decided to use simulation for nding
the optimal conguration of the scheduling tool.
The problem is to build a simulation tool, which will embrace the arrival of
customer orders and sequencing of production orders to meet these demands. An
important aspect is to model the production process itself in order to ensure that its
main stages are optimally loaded at all times. The anodising stage is known to be
particularly important for the overall production. Thus it has to be modelled in great
detail and used in order to test to what extent the overall lead time of the orders can
be reduced by optimisation of the anodising process stage.
The following key objectives are stated in this case study: (1) to model inter-
related business and production processes at the company and to determine the
overall lead time of orders, (2) to analyse and optimise business processes at the
planning department dealing with processing of incoming enquiries and planning
production orders, (3) to test the sensitivity of the overall production lead time to
improvements, in particular, to determine whether introducing specic sequencing
rules of production orders will decrease their total processing time at the anodising
process stage.
Moreover, a simulation tool is aimed to be used for testing the conguration of
the scheduling tool and for iterative optimising its performance off-line prior to its

2 Manufacturing System Planning and Scheduling 21
implementation and integration at the customer’s site. The envisaged scheme is
designed to complement and link together localised advisory systems previously
installed on individual areas of the production process.
The main impact of simulation is expected to be a higher system throughput
with lower product unit costs.
2.3 Modelling Approach
A custom-built business/manufacturing system model is created that simulates the
arrival of orders, their queuing and their ow through all steps of the production
process. For the overall coordination and schedule optimisation, each process stage
is modelled as a group of machines with an overall capacity per day or per week.
The model is built in a modular style so that each production stage could be further
modelled to a greater level of detail. As mentioned above, the anodising process
stage is known to be particularly important for the overall production. Thus this
production stage is modelled in a greater level of detail following successful valida-
tion of the initial model.
Therefore the model of the anodising process is rened and the individual ano-
dising tanks are described in detail, so that colour changeover and set-up opera-
tions could be studied more precisely. In this way, order queue ranking rules that
minimise colour changes are introduced and tested as to what extent the overall
lead time of orders can be reduced by optimisation of these rules at the anodising
process stage.
Next, the Preactor scheduling tool is coupled with: (1) a high-level business/
manufacturing system model, and (2) a detailed representation of the anodising
process stage, both of which were developed using production simulation system
ProModel [3] and used for nding the optimal conguration of the scheduling
tool.
2.3.1 A High-Level Business/Manufacturing System Model
In this section we will provide the conceptualisation and input data analysis for a
high-level business/manufacturing system model. It is aimed at modelling inter-
related business and production processes at the company in order to analyse and
optimise business processes at the planning department. These processes relate to
the processing of incoming enquiries and planning of production orders conrmed
by customers. The model is used to compare two alternative planning scenarios (see
Sect. 2.5) and analyse the benets of introducing an advanced production schedul-
ing and capacity optimisation tool at the company with the maximal response time
of 0.1 hour per enquiry.
Model conceptualisation. The custom-built entire business/manufacturing sys-
tem conceptual model is given in Fig. 2.1. The model simulates the arrivals of

G. Merkuryeva and N. Shires22
enquiries and their processing time; generates orders becoming conrmed by cus-
tomers and their planning time, and shows the queuing of the production orders for
processing. There are two types of incoming enquiries − pharmaceutical enquiries
and personal care enquiries, which are denoted as PH_Enquiries or PC_Enquiries,
respectively.
Production itself consists of the following processing stages: pressing, degreas-
ing, jigging, anodising and packing. In this model the production of orders does not
need to be modelled in detail. So, in each production stage the individual machines
are modelled as a group with an overall capacity per week. No queues are dened
for locations used to simulate different production stages in the system model.
The following parameters could be controlled in the system: the number of plan-
ners that process enquires from customers as well as respond to customers and plan
conrmed orders for production; the response time for enquiries, and planning time
for conrmed orders. These system parameters dene the controllable variables in
the simulation model.
Parameters such as time between arrivals of enquiries, customer response time
to conrm or cancel enquiries, the probability of an enquiry becoming conrmed or
becoming an order, and order processing time for different production stages could
not be controlled in the system. These parameters are regarded as environmental
variables in the model.
The system key performance indicators such as total revenue, an average lead
time, the percentage of cancelled enquiries and utilisation of planners dene the
model performance measures.
Datacollectionandanalysis. Based on the analysis of the historical data and tak-
ing accounts, their stochastic nature probability distributions given in Table 2.1 are
derived. For example, the time between arrivals of PC_Enquiries is exponentially
Fig. 2.1 The high-level business/manufacturing system

2 Manufacturing System Planning and Scheduling 23
distributed with the mean equal to 20, and processing time of the enquiries is uni-
formly distributed with the mean and half range equal to 35 and 5, respectively (see
ProModel distribution functions in [3]). These distributions are used in the model to
generate the time between arrivals of enquiries, processing times of the enquiries,
an average response time from a customer and actual planning time of conrmed
orders. About 33% of all incoming enquiries are PH_Enquiries. The probability of
enquiries becoming an order decreases as a function of the planning department
response time including enquiries queuing time and is given in Table 2.2. On the
other hand, the value of conrmed orders received by the company increases as a
function of the planning response time. In the case study, the average order value
is dened.
An average order lead time in each production stage is dened by the trian-
gular distribution with the following parameters: min = 1,080, mode = 1,440 and
max = 1,800.
Currently PH_Enquiries are processed by one planner, and PC_Enquiries are
processed by another three planners that spend about 70% of their working time on
planning operations. The working day is eight hours long starting from 9.00 a.m.
Planning staff employment costs per year are xed.
Model building. The entire business/manufacturing system simulation model is
built using the ProModel basic modelling elements such as locations, entities, arriv-
als and processing. A number of variables are dened as well. Some of these vari-
ables are counters which record statistics about cancelled enquiries, orders in pro-
cess, completed orders, etc. So-called processing variables are introduced to make
it easier to change processing times in the model.
Visualisation of the model is presented in Fig. 2.2. On-line and off-line statistics
are provided. Simulation outputs reecting the model dynamics (i.e. Waitingenqui-
Table 2.2 Probability of enquiries becoming an order
Enquiries becoming confirmed (%) Planning response time
50 < 1 hour
20 1–8 hours
10 24–48 hours
Table 2.1 Probability distributions (all values are given in minutes)
Data Distribution type Distribution
Time between arrivals of enquiries
PH_Enquiries
PC_Enquiries
Exponential
Exponential
E(60)
E(20)
Processing time of enquiries Uniform U(35, 5)
Response time from a customer Constant 24 * 60
Actual planning time of conrmed orders Uniform U(55, 5)

G. Merkuryeva and N. Shires24
ries, Completed orders, Total revenue) can be followed on the model main screen.
Results of conducted experiments are automatically saved in the model database
and presented in Excel spreadsheets.
In order to check if the model reects the real process adequately, a set of his-
torical data was compared with the data produced by the simulation model. It was
found that the model and the real process produced more or less identical results.
2.3.2 A Low-Level Anodising Process Stage Sub-Model
Model conceptualisation. The low-level anodising process stage sub-model [4] is
aimed at testing whether the implementation of specic sequencing rules of incom-
ing production orders will decrease their total processing time at a batch anodising
plant.
Batch anodising refers to anodising of series of small parts produced in batches.
The anodising process contains the following steps. First, the metal parts are batched
on racks. After batching the metal parts are degreased and cleaned. Then batches
of cleaned metal parts are put in a bath of acid where the oxide lm around the alu-
minium is created. After that the aluminium parts are rinsed with cold water. Then
the oxide lm around the aluminium is coloured with a spray. This spray, which is
also called as a dye, is typically a kind of paint, mixed with water. Dying can be
done in several steps in order to provide the right colour. Changing the colour of the
dying process is a bottleneck in a real system. Coloured parts are rinsed rst with
cold water and then with hot water.
The model itself simulates the individual anodising tanks so that colour change-
over, set-up operations and processing times can be modelled. Based on the his-
Fig. 2.2 A high-level business/manufacturing system model screenshot

2 Manufacturing System Planning and Scheduling 25
torical data about order processing, the most probable list of incoming orders to be
weekly processed is generated in the model. Specic sequencing rules of incoming
orders are simulated and tested in order to decrease the total processing at the ano-
dising stage. Production rate, which is dened as an average number of ight bars
processed per hour, and the frames utilisation coefcient are used to measure the
effectiveness of the anodising plant itself.
The anodising sub-model black-box diagram is presented in Fig. 2.3. The
sequence numbers of incoming orders that have to be processed in a week is con-
trolled in the model. The order quantity, part colour and used frame type for incom-
ing orders are regarded as environmental or independent variables. If these prop-
erties are given, the other properties of orders in the order list can be determined.
Other environmental variables are the number of frames in stock, the time it takes
to load and unload ight bars, the time it takes to set-up ight bars between the
processing of different colours and the processing time necessary to anodise one
batch of components.
The most important performance indicator is dened as the total processing time
of all orders in the order list. Among other performance indicators that could be
used to control an anodising process in the real system, the following performance
measures can be mentioned: average production rate, frame loading efciency,
ight bars utilisation and plant productivity.
Datacollectionandanalysis.First, based on the analysis of historical data about
the orders that were planned and processed at the plant in a certain period the gen-
eral order list is created. It includes the following input data: week number, order
number, order quantity, colour, frame type and frame capacity, the number of frames
in stock, number of batches and sequence number (Table 2.3).
The last four digits of the order number, Orderno., refer to the code of the colour
which the components should get. Each frame type has a different number of com-
ponents that can be placed upon it, which is called as Framecapacity. The number
of frames of a specic type available is called as Frameinstock. Only three frames
can be loaded on each ight bar.
Processing time of one batch of the components in a ight bar depends on the
program that is used in the anodising process is dened by a sequence number Seq.
no. in Table 2.3. Based on the input data analysis, processing times are described by
the triangular distribution and generated in the simulation model. For example, for
sequence 8, which is used by orders with colour code 0001 the triangular distribu-
tion with endpoints (54, 72) and mode at 58 is used in the model.
Fig. 2.3 Anodising sub-model black-box diagram

G. Merkuryeva and N. Shires26
Table 2.3 A fragment of the general order list
No. Week Order no. Order qty
(× 1,000)
Colour Frame
Type
Frame
capacity
Frames
in stock
Number
of
batches
Seq. no.
1 1 1135 1 0001 100 Bright Silver C1 1292 15 26 8
2 1 1135 1 0134 100 Bright Gold C1 1292 15 26 6
3 1 1407 0 0003 2 Bright Gold D2 2400 11 1 6
4 1 1135 1 0134 55 Bright Gold C1 1292 15 15 6
51 0803 0 0058 25 Bright Gold D2 2400 11 4 8
6 1 1210 1 0001 300 Bright Silver L2 2500 18 40 8
Table 2.5 A fragment of the input order list
No. Colour
code
Qty
(× 1,000)
Frame
type
Frame
capacity
Processing
time (min)
Process-
ing time
(mode)
Processing
time (max)
Batch
no.
Frames no. Frames
left
10058 28 7 3456 54 58 72 3 9 0
2 0003 225 2 1900 64 87 92 45 135 0
3 0001 224 6 3456 54 58 72 22 65 2
4 0001 711 3 2400 54 58 72 99 297 0
5 0058 139 6 3456 54 58 72 14 41 2
6 0001 93 4 2500 54 58 72 13 38 2

2 Manufacturing System Planning and Scheduling 27
Second, based on the general order list the most probable list of incoming orders
to be weekly processed in the model is generated. The number of orders in this
order list is xed equal to the average number of orders in a week. Frequencies of
order colour and order quantity as well as of the frame type to be used are derived
from the general order list data and dened by empirical distributions (see an exam-
ple in Table 2.4). For simplication it is assumed that order quantity and frame type
depends on the product colour to be anodised. Fitted probability distributions are
used to generate the most probable list of orders or so-called input order list. A frag-
ment of the completed input order list is given in Table 2.5.
Note that parameters of the probability distribution that t processing times (such
as minimum, maximum and most likely value), the number of batches that an order
should be split up in, the number of frames necessary to process all batches and the
number of frames left are also included in the Input order list. The Input order list is
generated in Excel spreadsheets that allow automated retrieval data from it within
the simulation experiments.
Model building. The anodising process stage sub-model is built using the Pro-
Model basic elements and includes three types of locations: a location where enti-
ties that are batches in the model arrive, another location where processed entities
move to and the number of locations where entities are being processed.
Figure 2.4 shows a screenshot of the model visualisation that is created by
animation of pictures that simulates order arrivals and storage as well as colour
change-over, set-up and order-processing operations. The user can follow the ow
of batches from the arrival location and analyse the current stage of the anodising
process for each order. Different colours are used for incoming and processed enti-
ties. Entities that are processed move on to the storage location.
On-line statistics are provided by three counters on the right-hand side of a
screenshot that display the following performance characteristics of the anodis-
ing plant: the number of orders that are left to process, the number of batches left
to process and the average number of processed batches per hour. Two additional
counters along with the ight bars indicate the current number and the colour of
the order that is currently being processed. Total processing time of all incoming
Table 2.4 Empirical probability distribution for order quantity (colour number 0001)
From To Probability
0 100 0.407
100 200 0.507
200 300 0.759
300 500 0.815
500 600 0.928
600 700 0.963
700 800 0.981
800 1000 1

G. Merkuryeva and N. Shires28
orders, frames loading efciency and plant utilisation can be found in the general
simulation output report.
In the case study, validation of the anodising process stage sub-model is not
described in detail. Note that similar to the entire business/manufacturing system
model, in order to validate this model a set of historical data was compared with the
data produced by the simulation model.
2.4 Experimentation
To identify the warm-up period, to select the replication length and the number of
replications, and set these options in simulation experiments, we refer the reader to
statistical methods of simulation output analysis and simulation options provided
by ProModel simulation software [3].
2.4.1 Planning Scenarios for Business Process Optimisation
To understand the entire business/manufacturing model behaviour and dene which
input factors have important impacts on the model outputs, regression-type simula-
tion metamodels were built in the case study. For example, the following regression
equation was received, which shows the effects of input factors to PC order lead
time in the system:
Lead time (PC) = 9277.03 – 21.05 * Enq + 4.83 * Ord + 0.62 * Enq
2
+ 0.41 * Enq * Ord,
Fig. 2.4 The anodising process stage sub-model screenshot

2 Manufacturing System Planning and Scheduling 29
where Enq and Orddenote PC_Enquiry processing time and order planning time,
respectively. As the result we conclude that the model outputs are more sensitive to
enquiries processing time rather than to orders planning time.
Then to investigate how sensitive the model outputs are to the changes in the
important inputs, these inputs were systematically changed and simulation outputs
were observed. It was stated that if the response time for customer enquiries could
be reduced by 5%, the total revenue of the company would grow by about 10%.
For business process optimisation within available system resources two optimal
designs of the system using the ProModel SimRunner® Optimiser were generated.
They dene the optimal combination of enquiry processing time and order plan-
ning time that maximises the total revenue and minimises the lead time indicator,
respectively. The results (see Table 2.6) show that the maximum revenue could
be achieved if enquiry processing time does not exceed 6 minutes. This could be
achieved by introducing the automatic Preactor supply chain planning server with a
maximal response time of 0.1 hour, or 6 minutes per enquiry.
To improve the planning process at the company, two alternative scenarios were
compared:
• Scenario1 in which the scheduling of individual areas of the production process
is provided – the current situation with the maximal response time equal to 1
hour per enquiry, not including queuing time
• Scenario2 in which an overall scheduling system coordinates all local busi-
ness and production processes – introducing the automatic Preactor supply chain
planning server
The results of simulation experiments (Table 2.7) show that the number of cancelled
orders in Scenario 2 can be decreased by 14–18%, which would cause the total
revenue or the total value of conrmed orders to increase at least twice. This can
be explained by a shorter enquiry processing time that provides a faster response
to the customer and leads to a higher probability for enquiries to become an order.
Table 2.7 Comparison of alternative planning scenarios
Lead time (min) Total revenue (€) Cancelled enquiries (%)
PH PC PH PC
Scenario 1 10,805 10,414 17,170,588.24 57 57
Scenario 2 9,793 9,617 41,758,823.53 43 39
Table 2.6 Comparison of two optimal designs
Enquiry
processing
time (min,
max)
Order
planning
time (min,
max)
Revenue € Leadtime,
PH (min)
Leadtime,
PC (min)
Maximised revenue (4, 6) (2, 8) 49,900,000 9,218.2 9,261.1
Minimised lead time (1, 11) (3, 7) 48,210,000 9,244.4 9,134.7

G. Merkuryeva and N. Shires30
Moreover, instead of four planners, only three would be needed if the new schedul-
ing tool were introduced. Thus, employment cost can be saved as well.
Notice that the total revenue value was estimated based only on observations
on the steady-state behaviour of the model. The counters for completed orders are
stated for the replications including the model warm-up period. The last one is
estimated almost by three weeks. The replication length is dened as twice as the
warm-up period. While the planning department works only on weekdays, the pro-
duction process continues 24 hours a day, seven days a week. After ten replications
the variance in the output variable such as average lead time is small enough to get
a half range of 5% average.
2.4.2 Testing Sequencing Rules
for Processing Production Orders
The scheduling of order processing at a batch anodising stage is to be interpreted
as a nite capacity scheduling problem. This is dened as the process of creating
an operation schedule for a set of jobs that are to be produced on a limited set of
resources. In the problem, the number of frames in a stock available for a specic
frame type and the number of ight bars that the frames are loaded on are limited.
Since this frame type is limited, it could cause queues of orders waiting for free
frames, while the ight bars could be empty. On the other hand, processing of pro-
duction orders with different colours could lead to multiple set-up operations, while
decreasing the number of necessary set-up operations will result in reducing the
total lead time at the plant.
For testing different order sequencing rules four simulation scenarios were intro-
duced in this case study (see Table 2.8). In Scenarios A0 and A1, single queue
sequencing rules are applied. Scenario A0 represents the initial situation, in which
the incoming orders are processed according to their arrival mode. In Scenario A1,
the orders with the largest quantity of components are processed rst. But in Sce-
nario A2, the orders wait in separate queues determined by order colour and single
sequencing rules are applied to orders within each queue. In Scenario A3, an order
sequencing rule combination is used in which the colours that appear less frequently
in the list are processed rst, while within the group of the same colour, the orders
with the largest number of components are processed rst.
Table 2.8 Simulation scenarios
Scenario Sequencing rules
A0 First-come, rst-served
A1 Largest order quantity rst
A2 Queuing by colour
A3 Less frequent colour rst–largest order quantity combination

2 Manufacturing System Planning and Scheduling 31
To implement sequencing rules for processing production orders in the simula-
tion model, the input order list described in Sect. 2.3.2 was rescheduled in the way
the scenarios describe. The difference between mean values of the total processing
time of all incoming orders was estimated from simulation experiments for scenar-
ios with specic sequencing rules and the initial scenario. The length of the simula-
tion run was dened to be equal to the time between the start of the week, which
represents the initial situation in the real system, and the time that all the week’s
orders had been processed. For each replication, the common random numbers were
used to simulate both scenarios, leading to a lower variance of the mean estimate.
The results of simulation experiments with the detailed model of the anodising
stage have demonstrated that introducing new specic sequencing rules for incom-
ing orders could provide signicant improvements. While comparing Scenario A0
and A1, 20 replications were performed for each scenario and the difference of two
means μA0–μA1 was estimated as 11.51 hours with 95% condence interval equal to
(3.82, 19.9) hours (see Fig. 2.5, a). This led to the conclusion that the A1 sequenc-
ing rule for incoming orders in a week could reduce the total lead time of this stage
by at least 4 hours, in some cases even by 19 hours. As a result, the production rate
of the anodising stage will go up by 10%, and a signicant increase in equipment
utilisation and reduction of unit manufacturing cost can be achieved.
At the same time, the condence interval for two other cases (see Fig. 2.5, b and
c) contains zero. These results show that there is no signicant difference between
the mean total processing times produced by Scenario A0 and Scenarios A2 and/or
A3, respectively, and there is no sufcient evidence to pick one alternative scenario
over another one.
Then what-if analysis was performed to test whether the implementation of Sce-
nario A1 is still an improvement if the number of frames in stock will be increased.
In this case frames are not considered as limited resources in the real system. The
results of comparison of sequencing rules with unlimited frames showed that Sce-
nario A1 will not make a signicant improvement compared to Scenario A0 (see
Table 2.9).
Table 2.9 Comparison of alternative sequencing rules with unlimited number of frames
Scenarios Mean difference
(hours)
95% confidence
Interval
Significant
A0 A1 0.01 (−0.55, 0.58) No
A0 A2 6.27 (5.85, 6.89) Yes
A0 A3 6.23 (5.59, 6.86) Yes
Fig. 2.5a–c Positions of the confidence
intervals relative to zero

G. Merkuryeva and N. Shires32
On the other hand, the orders queuing by colour in Scenario A2 will decrease
the total processing time at least by 5.85 hours. At the same time, there will be no
signicant difference between Scenarios A2 and A3.
2.5 Conclusions
This case study demonstrates that the modular simulation models provide an inex-
pensive tool for an overall guidance and testing of advanced scheduling middle-
scale software packages prior to their implementation at the customer’s site.
The modelling approach used in the case study – to test and optimise advanced
planning and control tools off-line by using simulation models rather than using the
real process – can be applied to many other software tools, to higher-level (MRP;
ERP tools) as well as to lower-level control tools (MES, warehouse control sys-
tems). On the other hand, the development of such relatively simple simulation
tools in different industrial sectors could also provide an inexpensive approach to
an overall guidance of small and medium-sized manufacturing towards the optimal
conditions without resource to high-cost integration of expensive ERP systems and
downstream control systems.
2.6 Questions
1. How can simulation help test and nd the best conguration of the scheduling
tool in a real system?
2. What is the range of scenarios for which simulation is used in planning and
scheduling of the manufacturing system?
3. What is the main feature of the modelling approach applied in this case study?
4. What are the most signicant differences between simulation models built within
this approach?
5. What are the characteristics of the simulation model used for business process
optimisation?
6. What are the characteristics of the simulation sub-model that is used for sequenc-
ing of the production orders at the anodising stage?
7. What does the condence interval express about the order sequencing rules at
the anodising stage?
8. Which techniques are used to validate the simulation models?
9. Dene the main operational and nancial benets of this study.
Acknowledgment This work was supported by the SIM-SERV Thematic Network project
‘Virtual Institute for Production Simulation Services’ (http://www.sim-serv.com).

2 Manufacturing System Planning and Scheduling 33
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