The authors present basis of simulation usage in managerial decisionsupport focusing on the supply chain processes. In the beginning the need for simulationis presented, then advantages and disadvantages of simulation experiments and thesimulation tools juxtaposition. Finally the chances of supply chain process simulationusing Logware software are presented.simulation, supply chain

Beata Åšlusarczyk

Sebastian Kot

English

Research Papers in Economics

Annales Universitatis Apulensis Series Oeconomica, 11(2), 2009 
 
 
932 
 
PROCESS SIMULATION IN SUPPLY CHAIN USING LOGWARE 
SOFTWARE 
 
 
Sebastian Kot
1 
Beata Ślusarczyk
2 
 
 
ABSTRACT: The authors present basis of simulation usage in managerial decision support focusing 
on  the  supply  chain  processes.  In  the  beginning  the  need  for  simulation  is  presented,  then 
advantages and disadvantages of simulation experiments and the simulation tools juxtaposition. 
Finally the chances of supply chain process simulation using Logware software are presented. 
 
Key words: simulation, supply chain 
 
JEL codes: C88 
 
 
Introduction  
Today high flexibility in supply chain is required because of shorter and shorter periods of 
order realization or wide order diversity. Adequate supply chain planning and efficient processes in 
supply  chain  can  ensure  the  flexibility.  The  role  of  transport  and  other  logistics  processes 
management is extremely significant. The transport connects supply chain elements: producers, 
suppliers, distributors and others. The aim of transport is to deliver particular sorts and quantities of 
raw  materials,  semi-manufactures  articles  and  products  in  the  right  time  and  right  place  for 
customers satisfaction. The effects of badly working logistics processes in supply chain are: 
•  high transportation costs;  
•  wastes of time and transportation means; 
•  high level of inventories; 
•  ineffective supply chain participants operations; 
•  long time of order realisation; 
•  incomplete utilisation of production capacity. 
Improvement in logistics process organisation including materials or products  flow often 
requires  high  investments  without  absolute  certainty  that  the  activities  will  bring  the  expected 
profits. Fortunately, there are useful tools now that can prevent spending money for misplaced 
activities or investments in material or products flow planning or operating. Simulation techniques 
used in material flow planning and operations are an effect of the IT systems evolution but not the 
only one. Today simulation techniques are used in logistics area for planning changes in every 
activity of the whole supply chain. Shapiro widely describes all systems existing in supply chain as 
those that can be modelled and simulated (Shapiro, J. F., 2001). They consist of supply, production, 
distribution, utilisation and all phases of technical systems life e.g. system planning, realisation and 
exploitation. They are enough various and require different software to apply, mostly. 
 
 
 
                                                 
1 Czestochowa University of Technology, The Management Faculty, Armii Krajowej 19B, 42-200 Czestochowa,          
e-mail: sebacat@zim.pcz.czest.pl 
2 Czestochowa University of Technology, The Management Faculty, Armii Krajowej 19B, 42-200 Czestochowa,         
e-mail: jagoda@zim.pcz.czest.pl Annales Universitatis Apulensis Series Oeconomica, 11(2), 2009 
 
 
933 
 
Simulation fundamentals 
Simulation is defined as an imitation of a dynamic process that has a place in a specified 
system; applying a model for experiment in order to achieve information that can be transferred to 
‘reality’  [  VDI  -  Verein  Deutscher  Ingenieure:  VDI-Richtlinie  3633.  Erfurt.    Simulation  von 
Logistik , MaterialfluB  und Productionssystemen. Grundlagen. Dusseldorf, 1992]. Simulation also 
can  be  presented  as  an  experimentation  form  of  a  analytical  model  that  gives  answers  to  the 
question: How the analyzed system will behave in specific situation. 
According Korzen and Janowski (Korzen Z., Janowski R., 1997) three types of simulation 
can be distinguished: 
1.  Simulation enabling decision effects prognosis - this type of simulation is a process 
phase of model construction, it is called first type simulation 
2.  Simulation  enabling  sensitivity  analysis  of  achieved  effects  caused  by  input  data 
change - it is called second type simulation 
3.  Simulation  called  third  type  simulation  enables  analysis  of  structure  and  model 
choice for output parameters and advisable input. 
Simulation  models  can  compensate  for  the  disadvantages  of  analytical  models,  but  not 
without sacrificing some of the advantages of the analytical models. Several methodologies have 
been developed to improve logistics performance and introduce supply chain management. Law and 
Kelton (Law A.M. Kelton W.D., 1991) also mention a number of advantages and disadvantages of 
simulation (Table no. 1) 
 
Table no. 1  
Advantages and disadvantages of simulation 
Advantages  Disadvantages 
•  Most  systems  with  stochastic  elements  are  too 
complex for analytical evaluation. Thus simulation 
is the only possibility.  
•  Simulation allows one to estimate the performance 
of an existing system under some projected set of 
operating conditions. 
•  Alternative  proposed  system  designs  can  be 
compared  to  see  which  best  meets  a  specified 
requirement. 
•  Better  control  over  the  experimental  conditions 
than when experimenting with the system itself. 
•  Allows one to study a system with a long time-
frame, in compressed  time, or even in expanded 
time. 
•  Each  run  of  a  stochastic  model  produces  only 
estimates  of  a  model’s  true  characteristics  for  a 
particular  set  of  input  parameters.  Thus  several 
independent  runs  of  the  model  are  required.  An 
analytical model, if appropriate, can produce the 
exact true characteristics. 
•  Expensive and time consuming to develop. 
•  The  large  volume  of  numbers  produced  or  the 
persuasive  impact  of  a  realistic  animation  often 
creates a tendency to place too much confidence in 
a study’s results. 
•  If a model is not a valid representation of a system 
under study, the results are of little use. 
Source: Law A.M. Kelton W.D.: Simulation modelling and analysis. 2
nd Edition McGraw Hill, 1991 
 
Most analytical models only take a few variables into account. For example, they may look 
at inventory and the cost of running out of stock, but ignore other costs such as order processing, 
handling  and  transportation.  Considering  sets  of  processes  in  the  logistics  chains  specifically 
focuses on the interrelated two-way flow of products (materials and services) and information with 
the associated managerial and operational activities to obtain a high degree of responsiveness. 
The complexity of analysing supply chain processes is much too large for analytical models, 
also due to the dependent demand in the supply chains. Furthermore, probabilistic demand in supply 
chains modelling creates extreme modelling complexities in a multi-echelon inventory situation 
(Silver E.A. Pyke D.F., Peterson R., 1998). 
There are many simulation computer program packages on the market and application of 
some can be limited for problem solving in defined areas.  Annales Universitatis Apulensis Series Oeconomica, 11(2), 2009 
 
 
934 
 
Tool choice for simulation experiment realisation is very crucial and it is depended on many 
factors (Grzybowski A., 2002): 
•  User’s experience 
•  Problem specification 
•  Required precision level 
According to Grabara and Szopa (Grabara J.K., Szopa J., 2002) the simulation tools can be 
divided into five main groups with the different characteristics of: model time construction, model 
control, precision, required learning time, application area (see Table no. 2). 
 
Table no. 2  
Simulation tools 
  Spreadsheets  Rapid  modelling 
tools  Simulators  Simulation 
languages 
Model  construction 
time 
minimum  minimum  medium  medium or long 
Model  control  and 
problem 
complication 
inefficient  in 
dynamic  problem 
simulation 
medium 
within  the 
framework  of 
program 
excellent,  in  every 
level  of  system 
complication 
Results  
defined and designed 
by user 
statistic possibilities, 
oriented  on  the 
report 
various,  depended 
on  program,  usually 
oriented on report 
defined  by  the  user, 
suitable  to  the 
problem 
Precision 
inappropriate  for 
dynamic  system, 
generally 
right for the general 
analysis 
in dependence on the 
assumption  excellent 
Tool learning time  minimum  medium  medium  medium or long 
Best  results’ 
application area 
static  and 
deterministic 
systems 
non  complicated 
systems,  in  respect 
of probability 
medium-complicated 
systems,  in  respect 
of  probability, 
specific application 
complex  problems, 
wide application 
Source: Grabara J.K., Szopa J., “Importance of technical processes’ simulation in engineering 
education” Intertech Conference, Santos, Brazil 2002 
 
Simulation of supply chain processes using Logware 
Logware is set of software programs that is useful for analyzing a variety of logistics and 
supply chain problems. It contains the modules that can solve among others - following problems 
(Ballou R.H., 2004): 
•  Forecasts  time  series  data  by  means  of  exponential  smoothing  and  time  series 
decomposition methods; 
•  Determines the shortest path through a network of routes; 
•  Develops routes and schedules for multiple trucks serving multiple stops; 
•  Finds  optimal  inventory  ordering  policies  based  on  economic  order  quantity 
principles; 
•  Locates a selected number of facilities by the exact center-of-gravity method; 
•  Solves the transportation method of linear programming; 
•  Solves general linear programming problems by means of the simplex method; 
•  Solves  the  mixed  integer  linear  programming  problem  by  means  of  branch  and 
bound; 
•  Simulates the flow of a product through five echelons of a supply channel. Annales Universitatis Apulensis Series Oeconomica, 11(2), 2009 
 
 
935 
 
Supply Chain Simulation (SCSIM) module is computer software to mimic the actual flows 
of product through a supply channel having four echelons ranging from factory to customers, as 
illustrated in Fig. no. 1. The product flows are replicated with a Monte Carlo-type simulation. A 
single, or aggregate, product is used and a single facility, or aggregation of facilities, is assumed at 
each echelon. The objective is to simulate supply channel performance and costs when various 
forecasting  methods,  inventory  policies,  transportation  services,  production  lot  sizes,  order-
processing costs, and the like are used throughout the supply channel. Reports and graphs are used 
to portray supply chain performance for different simulation runs. 
 
 
Fig. no. 1 – Supply Chain Overview 
 
A number of assumptions have been made in the operation of this simulation. These are not 
under the control of the user, but they are noted here for better understanding of the action of the 
simulator (Ballou R.H., 2004). 
•  All distributions are normal with a minimum average value of 1  
•  Any draw on a normal distribution producing a number less than 1 is truncated to 1 
•  The time period is one day with a week being 7 days, a month being 30 days, and a 
year being 360 days 
•  Quantities arriving at an echelon in the channel for a particular day are added to 
inventory before deductions from inventory due to sales are made 
•  The minimum value for demand, sales, and production lot size is 1 
•  Product volumes are in whole numbers 
•  Numbers  as  input  data  or  as  computed  values  greater  than  1  billion  should  be 
avoided as they may cause overflow errors 
•  All unfilled orders become back orders, not lost sales, until adequate inventory is 
available to eliminate the back orders 
The data inputs consist of simulation control information, demand projection data, sales and 
cost data, forecasting and inventory control methods, and transportation choices with statistical 
performance data. Any units of product flow may be used, but control the data scaling so that the 
numbers are neither too large nor too small. Time is always expressed in days. Costs, revenues, and 
profits are expressed in $. 
The input data can be divided on following groups:  
•  Initialization: seed number, length of simulation. selling price.  Annales Universitatis Apulensis Series Oeconomica, 11(2), 2009 
 
 
936 
 
•  Customer Demand Pattern: end customer demand, generate demand pattern, specify 
demand pattern.  
•  Inputs for Levels 1, 2, and 3: product item data, item value, inventory carrying cost, 
in-stock probability, back-order cost, customer/retailer/distributor/warehouse order filling, purchase 
order processing.  
•  Forecasting  method:  three  forecasting  methods  used  to  project  future  sales  at  a 
facility are allowed. Two are forecasting from a time series and the third is a user’s specified 
forecast -  
•  Reorder policy: inventory replenishment, reorder point, periodic review, stock-to-
demand. 
•  Factory/Source: production cost and lot size. production time, purchase cost, order-
filling cost.  
After running a simulation, the results of simulation are available in both graphical and 
report forms. Various combinations of graphs and reports can be selected. A variety of reports may 
be selected. Within the report, results are shown as a yearly average and for the simulated period. 
The yearly average is the average of a result for the selected period of the report, whereas the result 
for the simulated period is the sum of the result values over the simulated period.  
The reported financial items have the following meaning.  
•  Revenue. The selling price per unit multiplied by the retailer sales.  
•  Cost of purchased goods. The unit purchase cost times retailer unit sales.  
•  Gross  margin.  The  difference  between  retailer  sales  revenue  and  the  associated 
purchased goods cost.  
•  Production cost. Retailer sales multiplied by production cost per unit.  
•  Transportation costs. Transportation rate times the shipping volume moving between 
echelons within the channel.  
•  Sales order handling cost. The cost for a seller to process the sales orders received. It 
is the unit order filling cost times the unit sales volume.  
•  Order-processing cost. The cost for a facility to prepare a purchase order placed on 
an upstream seller. It is the purchase order-processing cost times the number of purchase orders 
placed.  
•  Inventory cost. The cost of an item in inventory times the inventory carrying cost 
times the average inventory level at a particular echelon.  
•  Back-order cost. The volume of units back ordered at an echelon times the unit back-
order cost.  
•  Total cost. The sum of the costs for a particular echelon.  
•  Net profit contribution. The difference between revenue and the sum of all supply 
channel costs. 
The reported performance items have the following meaning.  
•  Sales  forecast.  The  expected  sales  for  a  particular  echelon  as  projected  by  the 
selected forecasting method.  
•  Sales to channel member. The actual sales received by a channel member. This will 
usually be less than customer demand due to the inability to fill all of demand on request.  
•  Average inventory on hand. The average inventory level for the selected reporting 
period.  
•  Inventory  turnover  ratio.  The  ratio  of  a  channel  member’s  sales  to  the  average 
inventory on hand.  
•  Daily back orders. A channel member’s average daily back-order volume.  
•  Back-order  occurrences.  The  number  of  back-order  occurrences  that  took  place 
within the reporting period.  Annales Universitatis Apulensis Series Oeconomica, 11(2), 2009 
 
 
937 
 
•  Average  demand  filled  on  request.  A  service  measure  expressed  as  a  percent  of 
echelon member demand that could be filled at the time the demand occurred. Demand is either 
customer  demand,  in  the  case  of  the  retailer,  or  the  purchase  orders  placed  on  a  distributor, 
warehouse, or factory.  
•  Daily quantity on order. The average daily units placed as orders on an upstream 
seller that have not yet been received.  
•  Number of orders placed. Number of purchase orders placed on the upstream echelon 
for the selected period. 
 
Conclusions 
Simulation can make the research time and solution achieving shorter and more effective. 
Because of their possibilities and ability for verification of many alternative solutions for supply 
chain processes design and managing it is possible to choose the best solution without building the 
structures that is very expensive and time consuming. The computer support enable verification of 
processes and structures of supply chain in circumstances very similar to reality that allows for 
avoiding many expected and unexpected situations in future operations. 
It can be stated that simulation is useful and effective using computer support for processes 
improvement but It should be remembered that there is some disadvantages. They are as follows: 
user large experience, reality or problems simplification with results’ credibility estimation. 
 
References: 
1. Ballou  R.H.,  2004,  Logware     selected  computer  programs  for  logistics/supply  chain 
planning. Pearson Education International, Upper Saddle River, New Jersey; 
2. Grabara  J.K.,  Szopa  J.,  2002,  Importance  of  technical  processes’  simulation  in 
engineering education, Intertech Conference, Santos, Brazil; 
3. Grzybowski A., 2002, Computer Simulation in Constructing a Coefficient of Uncertainity 
in Regresion Estimation – Methodology and Results. Lectures Notes in Computer Science No 2338, 
Springer-Verlag, Berlin, vol. 2328; 
4. Korzen Z., Janowski R., 1997, Simulation researches problems in elastic production and 
logistics. VII International Conference –„Development tendency in production processes“ Zielona 
Góra; 
5. Law A.M. Kelton W.D., 1997, Simulation modelling and analysis. 2
nd Edition McGraw-
Hill; 
6. Shapiro, J. F., 2001, Modelling the Supply Chain. Duxbury Press; 
7. Silver E.A. Pyke D.F., Peterson R., 1998, Inventory management and production planning 
and scheduling, 3rd Edition, John Wiley & Sons; 
8. VDI  -  Verein  Deutscher  Ingenieure:  VDI-Richtlinie  3633.  Erfurt.    Simulation  von 
Logistik , MaterialfluB  und Productionssystemen. Grundlagen. Dusseldorf, 1992; 
 

PROCESS SIMULATION IN SUPPLY CHAIN USING LOGWARE SOFTWARE

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