Improving layout and workload of manufacturing system using Delmia Quest simulation and inventory approach

International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163
Volume 1 Issue 6 (July 2014) http://ijirae.com
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© 2014, IJIRAE- All Rights Reserved Page - 52
Improving layout and workload of manufacturing system
using Delmia Quest simulation and inventory approach
Elena-Iuliana Gingu (Boteanu) Prof. dr. eng. Miron Zapciu
Machine and Manufacturing Systems Department, Machine and Manufacturing Systems Department,
POLITEHNICA University of Bucharest POLITEHNICA University of Bucharest
Abstract—This paper describes a case study of analysis and optimization of the facility layout in a manufacturing cell
using a systematic search method and a Quest computer simulation model with graphical representation of the
manufacturing processes. The simulation model objective was to obtain Layout design to achieve a high productivity in the
flexible manufacturing system (FMS), to determine bottleneck locations and what the optimal batch size should be. The
Quest software proved to be a powerful tool in assessing what changes should be made to a manufacturing cell before
incurring manufacturing improvements and/or performing actual capital investments. The aim of this study is to get
an understanding of the cell and its behaviour regarding production and to use the simulation software to change,
analyse and improve the cell.
Keywords— simulation, Delmia Quest, facility layout, inventory, costing.
I. INTRODUCTION
In today’s fast paced world of manufacturing system, everything changes fast and new challenges require new
solution, which should be more universal, flexible and provide more possibilities to react to the continuous changes. The
situation of simulation modelling solutions and tools are effectively used to address new issues and challenges these
which are more flexible, feasible and provide seamless integration of various simulation approaches. Discrete event
simulation has been one of the most valuable technologies for assisting in decision evaluation in simulation and
modelling. Simulation can be used in many areas such as automatic control, logistics, mechanics and production. In this
study, simulation refers to production simulations. Production is a wide area and many different software systems exist to
assist in the production. Using simulation gives a better possibility to early on in the project determine the total
investment cost but also to answer questions such as cost for running and productivity of the system. Different solutions
can be compared and the best alternative can be decided. Simulation can reduce the time for development and
installation. Simulation also provides fewer disturbances in the start up of the production. Quest can be used to simulate
and get data for everything related to production systems, such as buffers, status, queues, production times and so on. A
correct built-up simulation can provide answers to many questions such as, where to set ordering points, number of
operators needed, number of parts needed, what happens when a machine breaks or if one operator is sick and so on.
II. SIMULATION IN MANUFACTURING CELLS
A. Manufacturing cell
Manufacturing cell is an important part of manufacturing system. It can not only meet the demand of market for multi-
variety and low-volume production, but also adapt to the rapid changes of market and so improve the system’s flexibility.
Therefore, more and more researchers at home and abroad are paying attention to it. So far, there have already been in-
depth research and fruitful achievements about the design of manufacturing cell. The traditional design methods put
emphasis on the selection of equipment and further optimization of block layout. In current years, genetic algorithm has
been introduced into the developing of manufacturing cell by more and more researchers. However, this method usually
focuses on the optimal group of facilities. Queuing theory, also called stochastic service theory, is a subject that concerns
and solves the congestion resulting from stochastic factors. It was applied to the study of manufacturing system in 60’s
last century. To design a manufacturing cell based on queuing theory, we can build various of models to calculate those
involved parameters of servers (e.g. the number of servers, the optimal productivity) and then we choose equipment or
determine their running conditions according to these data. As a result, we get the manufacturing unit that is relatively
proper and satisfying [19].
B. Simulation
“Simulation is the imitation of the operation of a real-world process or system over time. It involves the generation of
an artificial history of a system and the observation of that artificial history to draw inferences concerning the operating
characteristics of the real system.”
Discrete-event simulation is a collection of events that happen in chronological order and change the system’s state.
The state of the system (or part of it) is changed instantly when an event happens. Discrete-event simulation models are
used to study how the system works during the period of observation.
Discrete-event simulation models are commonly used in the industry for the following purposes:
- Capacity calculations.
- Analyzing throughput and lead times.

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- Layout-planning.
- Balancing production.
- Supporting investment decisions and as risk-management tool.
- Identifying bottlenecks and testing out control techniques.
C. Simulation software
Since the 1960s simulation has been used in the mining industry mainly in relation to transport systems, mining
operations, mine planning and production scheduling. Easy-to-use animation is one of the primary reasons for the
increased popularity of simulation modelling in mining. Simulations are developed either in general purpose languages
such as FORTRAN or Pascal, or in simulation languages such as General Purpose Simulation System (GPSS). However,
in recent years several PC simulation languages have been developed that can be used to model mining systems. These
include GPSS, AutoMod, Arena, SimFactory, Clam, Taylor II, SLAM and QUEST [16].
A discrete-event simulation model can be created in different ways depending on the available resources and
objectives of the model:
- General programming language (C++, C#, Visual Basic, Java & etc.)
- Simulation specific programming language (Arena, Simscript, etc.)
- Off-the-shelf, tailored discrete-event simulation programs, such as o 3DCreate, Automod, ProModel, Quest,
Taylor/ED, ProcessModel, etc.
D. Delmia Quest
Delmia Quest is the digital manufacturing platform subsystem of Dassault Company based on discrete events. It
provides simulation environment based on the delivery of materials, processing and storage. It contains material element
for rapid modeling such as machine tool, buffer, treatment process, failure rate, maintenance, operator, path and material
export, which can help users simulate and analyze the process flow in 3D factory environment [20].
In a Delmia-QUEST model, inter alia, there are:
– Parts, the entities that move through the system, occupying resources and undergoing processing. They may
represent goods, customers, orders, tasks or messages between resources.
Fig. 1 Parts
– Sources, the entities that create parts and release them into the simulation; sinks, the entities that remove parts from
the model.
Fig. 2 Source
– Buffers, entities used to represent the locations where parts are stored or where they queue to get access to other
Fig. 4 Machines
The inputs for the machines are the parts representing the activities to do. In QUEST, each real task has its own
corresponding part.
- Labor is required by the processes defined for the machines

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resources such as machines;
Fig. 3 Buffers
– Machines, the entities that process parts.
Fig. 5 Labors
- Sink, collect the processed parts for their exit from the model.
Fig. 6 Sink
Quest provides three-dimensional simulation environment for the analysis of production process flow and production
efficiency. The Quest model must be established before simulation and analysis. The following three steps are basic steps
to establish a Quest model. Firstly, three-dimensional geometrical model of production resources object such as
processing equipment, logistics equipment and operator must be established, making production line in virtual
environment similar to real production line. Quest contains initial model of common production equipment and logistics
equipment, such as processing equipment, buffer station, feed station, AGV, crane, conveyor, labor and so on. Geometric
model should be placed in three-dimensional space according to workshop layout, which achieves virtual physical
modelling of the entire workshop. Secondly, object behavior, technological process and system operation rules of
production resources should be defined. The object behavior refers to the loading, processing, and unloading behaves of
the processing equipment. Logical order of workpiece is defined by technological process of manufacturing system,
making independent device in the system connected into a whole process logical [10], [11]. System operation rules can
ensure the normal operation of the simulation model and avoid conflict in the system. Finally, production line is
simulated based its model, real-time utilization and average utilization of the key equipment and labors can be obtained.
The data of logistics production beat and stacking situation can also be seen clearly. According to these data, the
bottlenecks in the production line can be found and then the layout of the production line and logistics scheme can be
optimized [5].
E. SCL and BCL
The conceptual manufacturing layout used the modelling languages, which are Simulation Control Language (SCL)
and Batch Control Language (BCL). Manufacturing design layout is developed by composing the control language and
synthesizing the structures: control, data, and operational. The modelling language is based on a set of object classes that
simulates the behaviour of a real system component.
The SCL provides modelling rules that govern the action and all aspects of element in order to meet the unique needs
of designed model and show determination of the outline structure in a fabrication manufacturing layout. The SCL logic
tracking functions are compiled and appended into the file-based by associating the modular procedural programs with
specific resource entities [13].
SCL code is a programming language similar to the more famous PASCAL. Thanks to the written logics and
procedures the developed model now presents the following advantages:
– It is able to builds a suboptimal solution through a new constructive heuristic algorithm designed and implemented;
– It is easy to use by non-expert personnel;
– It requires in input a unique and simple file: an Excel datasheet and provides in output Gantt charts ready to be used
in the workshop.
In SCL anything that doesn’t already exist in Quest can be created, this makes Quest very flexible and versatile.

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Contrast to the SCL, the BCL is used to create a series of runs with alternative scenarios without interruptions,
furthermore it has been developed beyond the interface control stage to edit or modify the parameters of simulation
environment when manipulate the model. BCL will create a series of runs with alternative scenarios without
interruptions.
The BCL commands were typed in one at a time, which will change an existing model which is part of the QUEST
software. Elements in BCL codes of the model are derived from the sub-data file content. There are BCL macros were
assigned for the model in the QUEST modelling environment. This utilizes the BCL macro function to execute BCL
codes in QUEST. Upon calling up the BCL macro, the BCL codes executed and generated line by line into the QUEST
interface. The process of implementing BCL function for model designed, QUEST software must be loaded first with a
file of BCL Commands. A text of BCL commands are specified as providing the initial instructions to QUEST which
includes building or reading a model and modifying and running the model. Almost all BCL commands can be apply
through SCL programming [12].
III. SIMULATION MODEL
In this study is presented a simulation in Delmia Quest software which is based on scenarios ”what if…” Delmia
Quest is used for model validation, as it was previously indicated in the studies, in the sense that a valid model will be
able to produce a reasonable prediction of the system’s performance [8].
The simulation corresponds to a nine hour workday consisting of eight hours of actual work and one hour of breaks.
The amount of time the laborer takes to move between each assigned machine must be established: Constant on the
Distributions, 10 seconds. The distributions to describe the time between failures and the time to repair for this failure
class are defined: Exponential from the Distributions, a Mean value of 3600 sec. The time to repair is defined: Uniform
from the Distributions, a Minimum Value of 180 sec and a Maximum Value of 300 sec.
A. Facility layout
Facility layout means planning for the location of all machines, utilities, employee workstations, customer service
areas, material storage areas, aisles, rest rooms, lunchrooms, drinking fountains, internal walls, offices, and computer
rooms, and for the flow patterns of materials and people around, into, and within buildings. Facility layout represents
physical layout of the fabrication lines, machines, workstations necessary for the production process of conversion from
fabrics, factories, industrial installations. Facility layout is a placement of the means of productions used to create
products. Facility layout is composed of product, its process routing, machine and some space. Different combinations of
these entities and their activities affect the type of facility layout. Considering the criteria of material handling route, the
types of facility layout are classified into three types: single-row layout, multi-row layout and loop layout. The single row
layout includes three shapes such as linear, U-shape and semi-circular [17]. In the linear layout, there may exist
bypassing and backtracking. Backtracking is the movement of some parts from a machine to another machine that
precedes it in the sequence of placed machines in a flow line arrangement. Bypassing occurs when a part skips some
machines while it is moving towards the end of a flow line arrangement [18].
For example:
Figure 7 displays the current layout with five labors arranged in a linear layout. Each labor works in his own machine.
Fig. 7 Current Layout: Linear layout
Figure 8 displays the improved layout with only three men power and the layout is designed to be in a U-shaped.

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Fig. 8 Improved Layout: U- shape layout
After a run simulation of 32400 seconds, it is noted that, in both cases there is the same number of parts, 394 parts.
Fig. 9 Element production
In the Linear layout there are five labors for the processes. From the observation, labor’s capability has not been used
effectively. In U- shape layout there are only three labors for the processes.
Fig. 10 Labor utilization efficiency in linear and U shape layout
0
10
20
30
40
50
60
70
80
90
100
Labor1_1 Labor2_1 Labor3_1 Labor4_1 Labor5_1
Utilization(%)
U-shape layout
Linear layout

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After the simulation is observed that only 3 labors of U-shaped layout made parts just as many as 5 labors of linear
layout.
B. Bottlenecks
The bottleneck is the resource that affects the performance of a system in the strongest manner, that is, the resource
that, for a given differential increment of change, has the largest influence on system performance. This is an operational
definition that can be tested via discrete event simulation using finite difference sensitivities of throughput (or other
performance metrics) to individual resource changes. It is essentially based on the partial derivative of the system
capacity with respect to an individual resource’s capacity, that is, the rate of change in system capacity for unit change in
resource capacity [4].
To see how the machine times affect the cell the bottleneck needed to be determined.
So, the bottlenecks in our study are:
- Machines, which are idle in the manufacturing process;
Fig. 11 Machine utilization efficiency in U shape
- Machines, which are overload and sequential inputs;
- Parts, which pass to their operation from one cell to other. This are the biggest inter-cell movement resources.
C. Inventory
Many companies are adopting lean manufacturing strategies to reduce waste while improving processes as well as
trying to reduce inventory. However, in reality their production processes continue producing to stock due to the
economic benefit. The major reason for choice of make-to-stock or large batch production is because of the significant
setup cost and setup time that might result in lost production and decreased production capacity [14], [15].
The optimal size of the batch can be determined considering many variables such as setup times, setup costs, forecasts
of demand, costs for inventory, process time and so on [9].
The batch size or run size, Qi, for product i is calculated from:
= (1)
where X is the number of cycles per week.
The total amount of time required to produce a single batch of each product in one cycle is then calculated from:
= ∑( + ), i=1,n (2)
The ratio Qi/pi is measured in hours since Qi is a production lot size divided by pi, the hourly production rate for
product i, and is equal to the number of hours to produce lot Qi [7].
The required scheduled hours per week, ts, is then derived from:
= ∑( + ) , i=1,n. (3)
We can now calculate maximum inventory (Bi) for part i:
= (1 − ), (4)
where ui is the hourly customer usage rate for product i and it is determined from:
= . (5)
Total annual cost is now defined as the sum of the three cost components:
Total annual cost= Annual cost of production labour +Annual cost of changeover +Annual cost of inventory,
where annual cost of production labour is:
Annual cost of direct production labour = ∑( ), i=1,n. (6)
We should caution that the above cost may not be realistic due to possible idle time where the required weekly
scheduled hours (ts) is less than the maximum allowable weekly hours (tmax) and workers must be paid due to

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contractual agreement, service, maintenance, etc. [2], which depends on the number of hours the facility is operating per
working day. The annual cost of changeover is obtained from = ∑( + ), i=1,n
and annual cost of inventory is equal to:
Annual cost of inventory = Annual cost (Average maximum inventory+ Average unbalanced inventory + safety stocks),
where the unbalanced inventory for part i is as follows:
=
( )
, <
0, ℎ
. (7)
For example:
= 96, = 120, = 6000
= (120 − 96)
6000
120
= 1200
Finally, the annual cost of inventory is calculated as follows [3]:
Annual cost of inventory= ∑ ( 1 − + ) + , i=1,n. (8).
Where the notations represents:
- X =Number of cycles per week.
- di =Weekly customer requirements (demand) for product i.
- pi= Hourly production rate for product i. This represents the average of good products produced per hour when
the system/machine that is being scheduled is running. It should not include time lost for changeovers.
- Ssi= Safety stock for product i.
- Li = Number of operators required for producing product i.
- Si= Setup (changeover) time (hours) for product i. The time interval from the last good product produced on the
outgoing product to the first good product run in the incoming product. This should be the combination of
changeover time plus the time required to start producing the first good part.
- Sai= Additional changeover (labour-hours) for product i. This can include both direct and indirect labour and/or
salary hours dedicated to completing a changeover in addition to operators.
- Ci= Inventory value of product i.
- Icap= Cost of capital. This is sum of all quantities: cost of capital plus taxes and insurance plus cost of storage
plus cost of breakage plus cost of spoilage.
- tmax =Maximum weekly hours available for production. This is the maximum scheduled operating hours
available for production and changeover based on management directives, contractual agreements, etc.
- tc= Customer weekly work hours. This represents the production hours that are to be worked by the customer or
the next operation.
- W= Hourly wage. This is the average wage (including benefits, overtime, etc.) that is paid to production
operators and support personnel (skilled trades) required to be present during the production for the
system/machine being scheduled.
- N= Number of working weeks per year.
- n= Number of different products i=1, 2, . . . ,N.
- Imax= Maximum total allowable inventory. This represents any constraint that is required on the level of total
inventory for the system/machine being scheduled. This could be as a result of space limitations, management
directive, rack constraints, preventative maintenance requirements or perhaps other reasons.
- Qi= Scheduled lot (batch size) for product i. This is the amount to be produced in each cycle for product i.
In Delmia Quest software, we consider the inventory (stocks) the parts on the buffers [1]. Buffers in Quest are used to
represent the locations where are stored or where they queue to get to other resources such as machines. A buffer might
thus represent a storage location in a warehouse, the hopper or buffer feeding parts into a machine or a queue for
customers waiting in a bank.
The Initial Stock button allows us to set the numbers and classes of parts that are already present in the buffer when
the model run begins. The default is zero. This parameter allows a buffer to begin with any number of any part classes
that has been defined in the model [6].

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Fig. 12 Buffers – State Times in U shape
D. Costing
Manufacturing cost has two classify methods. The first one classifies the manufacturing cost into fixed costs and
variable costs. The second separates manufacturing cost into: (1) direct labor, (2) material, and (3) overhead. Cost
Macros is used to analyze the running cost of various resources in a system after running a simulation and to calculate the
product cost of a system. These macros calculate the cost of the product based on the principle of ABC (Activity Based
Costing) [6]. For example, we made the cost analysis for five machines in U shape model using Delmia Quest software.
TABLE I
COST ANALYSIS REPORT
COST ANALYSIS REPORT – Machines
Element name : Machine1_1
Total Cost : $ 53.60199
Variable Cost : $ 48.465
Fixed Cost : $ 5.136986
Power : $ 3.3
Coolant : $ 0.165
Labor : $ 45
Maint : $ 0
Setup : $ 0
Total Cost : $ 53.02449
Fixed Cost : $ 5.136986
Power : $ 2.75
Coolant : $ 0.1375
Labor : $ 45
Maint : $ 0
Setup : $ 0
Total Cost : $ 57.04948
Fixed Cost : $ 5.136986
Power : $ 6.583333
Coolant : $ 0.3291667
Labor : $ 45
Maint : $ 0
Setup : $ 0
Total Cost : $ 52.43678
Fixed Cost : $ 5.136986
Power : $ 2.190278
Coolant : $ 0.1095139
Labor : $ 45
Maint : $ 0
Setup : $ 0
Total Cost : $ 54.39767
Fixed Cost : $ 5.136986
Power : $ 3.283333
Coolant : $ 0.1773535
Labor : $ 45
Maint : $ 0.8
Setup : $ 0
MODEL: Ucosting.mdl
Run Time : 8.999998Hour
0
100
200
300
400
500
600
Buffer1_1
Buffer2_1
Buffer3_1
Buffer4_1
Buffer5_1
State Times Idle State Times Busy - Processing
State Times Blocked - Wait Block

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Fig. 13 Cost analysis –Machines- in U shape
IV.CONCLUSIONS
The aim of this article is to analyse and manage the manufacturing flows in different configuration of cells, in order to
improve the productivity, to reduce the costs and to obtain required stocks where we have a series production. By using
simulation in Quest not only one variable at the time could be tested but also how combined changes affected the
production. The ability to test and measure the results that followed often lead to the discovery of other things to
improve. After a while the ability to forecast the effects of the changes increased. At the end of the project it was
surprisingly easy to predict how the system and the number of parts produced would respond to different changes. Quest
gives the ability to use graphs and charts to visualize almost any data imaginable from the cell. With the right model it
will only take a few seconds to get the same information that would take days or even years to measure out of the actual
production cell.
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Improving layout and workload of manufacturing system using Delmia Quest simulation and inventory approach