2011 Simulation White Paper

Using Simio in the Design of Integrated Automated Solutions For Cement Plants

VIK, Pavel; DIAS, Luis; PEREIRA, Guilherme; OLIVEIRA, José and ABREU, Ricardo


This paper deals with the use of a discrete simulation tool (SIMIO) in the production system design of a cement plant. This work is intended to be relevant in the specification of a proposal of an internal logistic and monitoring weighting system (Cachapuz - SLV Cement).

Through this integrated approach (Simulation, Logistic and Weighting System) it is possible to test and validate system changes and their impact in the overall system performance. These changes will represent different scenarios, involving resources availability, layout restrictions, queues area dimensioning, equipment reliability, truck transport control logic, and parameters.

The model developed will guarantee efficacy and efficiency, helping to design new logistic processes through the integration of a global monitoring weighting system in a cement plant. (Vik, 2009)

Keywords: Layout Systems Design; Simulation;
SIMIO; Internal Logistics


This project is focused on improving internal logistic processes in cement production plants. The main will involve the integration of the SLV Cement logistic system and a discrete simulation software tool (SIMIO). This approach will help finding a high performance configuration and control of logistic components in cement plants. These components include weighting systems in both entrance and exit gates, registering and managing customer orders and requirements, truck flow control, etc.

Our project focuses on the identification of bottlenecks in the system, finding a set of possible solutions and choosing the best one. It is used computer simulation for this purpose. (Francis 1992)

1.1. SLV Internal logistic system

SLV Cement is a complete logistic system for cement plants developed by Cachapuz [www.cachapuz.com] This automatic and integrated logistic system deals with all the processes since the arrival of a truck to cement plant to the shipping of cement. At the end of the process, even the administrative tasks like and printing the necessary documentation are performed automatically. Another interesting SLV feature is the complete integration with SAP business software (ERP).

This proposed fully integrated logistic system will permit to increase throughput (trucks per unit time) and reduce the number of operators needed, thus avoiding human errors.

1.2. Discrete computer simulation

Discrete computer simulation is the act of imitating the behavior of an operational system or process using an analog conceptual model on a computer.

The arguments below will help understand why simulation would be a useful tool:

  • All processes have stochastic behaviour (Kulturel 2007);
  • It is a complex system with several resources and internal non deterministic conditional routing decisions;
  • 3D graphic and animation is relevant for easy demonstration and presentation Need for simulating crashes and breakdowns in real processes (e.g. printer breakdown, electric failures, human errors etc.)
  • Need for analysis of time dependent patterns of demand and patterns of facilities/resources available

Furthermore the following issues will identify why simulation would be relevant in the context of a cement company:

  • Currently used logistic application manages the trucks flows in the plant
  • It helps answering what-if questions, checking(testing) the impacts of system changes
  • The plant processes and control logic are in the minds of managers and changes are made based on their knowledge (experience) and not on proved scenarios.

1.3. SIMIO

SIMIO is a quite new simulation tool; it was developed in 2007 and represents a new approach in simulation- object orientation. Modelling is based on describing system's objects and system behaviour is evolved by interaction of these objects. (Pegden 2007, www.simio.com)

SIMIO supports:

  • Creating 3D animation by one step, importing and 3D objects from Google 3D Warehouse (snapshot in - SIMIO model with animation3DFigure 4)
  • Importing data from Excel worksheets (snapshot in Figure 2)
  • Writing own logic function (e.g. priority rules) in many languages (C++, Visual Basic, etc.) (snapshot in Figure 3)
  • Creating of own intelligent objects and libraries

For this project, SIMIO simulation software was chosen (other simulation tools are mentioned in Dias et al 2007) - for mentioned properties and also for these reasons:

  • testing this simulation tool for setting it as a part of currently used internal logistic system
  • testing new possibilities for introducing this new simulation tool for educational purposes in our department


As an illustrative example of the mentioned integration principle and for an easy understanding, we chose a simple model of a real cement plant and its logistic processes. This solution is based on general usage in any cement (or similar) kind of factory.

Our simulation approach included the following steps (Muther R, 1973), (Taylor, 2008), (Zelinka, 1995):

1) Definition of project aims

  • Definition of exact project targets according customers requirements
  • Setting of system's borders and level of detail
  • Team building and its responsibility

  • 2) Processing of input data

  • Technical data (facilities data, product data, information about material flows, production areas, breakdowns, shifts etc.)
  • Organization data (production scheduling)
  • Business data (costs, orders)

  • 3) Creation of simulation model

  • Conceptual model (schematic)
  • Computer model

  • 4) Simulation run and experiments

  • Validation and verification of model
  • Setting of parameters, length of simulation run

  • 5) Interpretation of results and implementation

  • Data analysis
  • Interpretation of results, their presentation and comparing suggested alternatives and scenarios (graphs, tables, 3D animation)
  • 2.1. Short description and definition of project aims

    In Figure 1 there is a schema of this system. There are several types of trucks according to the kind of loads (incoming trucks with raw material and outgoing truck with final cement products)

    Figure 1 - Schema of simple production system

    These trucks must go through the entrance gates where they are registered and weighted. After that, they are sent into correct location in the plant for loading or unloading material. Then, trucks are weighted again and after final checking, they can leave the plant through the exit gates.

    Through a preliminary analysis, some predictable problems would arise. Mainly, in this type of factories, the length of queues in some specific factory facilities - entrance gates, for example, always constitutes a relevant problem, causing long waiting times for customers. Usually this problem is due to inadequate number of facilities/resources available (e.g. loading places in warehouse), to long loading times, to a bad organisation of work flows that could lead to:

  • Wrong destinations associated to trucks, once inside the plant
  • Trucks waiting even when facilities are available
  • Bad administrative options and absence of modern registration technologies as magnetic cards
  • Traffic jam inside plant
  • Incorrect loads
  • Breakdowns of key facilities
  • Inadequate Production scheduling

  • Though, computer simulation could be a powerful tool to improve processes and draw suggestions for modern control systems implementation (Benjaafar, 2002)

    2.2. Processing of input data

    For a correct analysis and the creation of an adequate simulation model, it is necessary to use valid data as:

    • Definition of operations (inputs and outputs, production times etc.);
    • Productive facilities and their properties (capacity, transport speeds, breakdowns statistics etc);
    • Layout specifications and graphical models for entities and facilities (for realistic animation can be used 3D models of facilities, downloadable from Google 3D Warehouse)
    • Possible truck routings through production facilities and respective control logic
    • Workers and their properties (capabilities)
    • Production scheduling
    • Demand patterns (rate tables of arriving trucks)
    • Patterns of failures
    • Shifts management

    These data are processed for making analysis and formatted to SIMIO as it is shown in the Figure 2 (rate table of trucks within a day and example of production times).

    Figure 2 - Input data (arrival rate and distribution
    function) and its representation in SIMIO

    2.3. Creation of simulation model

    For the creation of a simulation model, standard elements as source, process and sink were used, connected by a set of paths. Four main processes were identified:

    • Loading request registration
    • Waiting for calling to entrance

    Figure 3 - Flow control (logic diagram - example) and Routing table

    SIMIO supports 3D graphics and animation. It is possible to import plant layouts in 'dxf' format and to use Google 3D Warehouse library for inserting 3D object models, creating realistic 3D plants. In Figure 4, there are some screenshot examples for this project.

    Figure 4 - SIMIO model with animation 3D

    2.4. Simulation run and experiments

    One of the project aims is to find out adequate sets of parameters for system configuration:

    • number of input gates
    • number of loading positions in the warehouse
    • number of output gates

    SIMIO permits to define a table of different scenarios and run several experiments (Figure 5).

    Figure 5 - Experiments table

    Then a comprehensive report is generated. In this short prototype described, the most important performance indicators are:

    • throughput of trucks (number of trucks entered and leave plant during time unit)
    • facility usage
    • waiting times of trucks for operation

    Results of these experiments are shown in the table in Figure 6. Each row represents the results of one experiment according to parameters shown in Figure 5.

    Figure 6 - Results of experiments - resource usage and throughput analysis

    2.5. Interpretation of results and implementation

    Following scenarios defined in Figure 5 and results shown in Figure 6, it is possible to conclude that the bottleneck of the current system is the number of loading places in the warehouse. Apparently, the number of gates used is adequate and it does not influence on the total system throughput. It was previously thought that, due to the high number of trucks waiting in the entrance gate, the number of gates would be critical to the overall system performance. Instead, it is now clear that the main problem is concerned with the number of loading places in the warehouse - in fact this number of loading places do affect system performance.

    Figure 7 shows graphically the number of trucks inside the plant during a whole week. These data were obtained by exporting appropriate arrays from SIMIO and confirm previous set of conclusions.

    Figure 7 - Graph of number trucks inside the factor

    For trucks behavior analysis, a special array was created in order to record important moments of each truck in the system (for example arrival time for each truck, registering time, calling time, operation times, dispatching time etc.) Results of this analysis are:

    • Total time in plant
    • Waiting time in queues
    • Loading times

    Examples of these results are shown in Figure 8.

    Figure 8 - Analyses of overall truck behavior



    The usage of an internal logistic system and discrete computer simulation for the design of this type of factory seems to be a good approach. It is possible to virtually implement a logistic control system to an existing factory and analyze corresponding impact without any type of physical intervention in the real factory - it is also possible to completely design a brand new factory.

    The previous simulation model could also be used to:

    • Testing impacts of fails, breakdowns or crashes and predicting ways of overcoming these situations
    • Creating 3D realistic detailed animation for presentation
    • Avoiding bad design decisions through previous tests with the simulation model
    • Testing system stability
    • Evaluating stochastic influences into the design of the system
    • Achieving a global system configuration

    With our integrated approach, it is possible to achieve a global system configuration and find the best solution for each set of resources (e.g. facilities, space, human resources) as well as for reducing customer (trucks) waiting times. These first results do seem to be a good motivation for next steps in the use of simulation.
    Next steps for this project would then include the definition of the entire detailed model and respective full implementation of the logistic system. Also full realistic 3D animation would be created.

    This project is part of research cooperation between University of Minho (www.dps.uminho.pt) and Cachapuz (www.cachapuz.com). Cachapuz is member of one of the biggest European weighing groups (the Bilanciai Group) and have a strong positioning in the integration and automation of the logistic operations on Cement plants.