Always distinguish clearly between a SIMUL8® Label (what some other software, simulation textbooks, and technical articles call an “attribute”) and a SIMUL8® Information Store (what some other software, simulation textbooks, and technical articles call a “variable”). A Label belongs to an Entity (Work Item); each Work Item has its own copy of the Label. For example, if refrigerators are being manufactured, and some have “freezer on top” and some have “freezer on bottom,” a Label may be used to distinguish the Work Items. An Information Store belongs to the model as a whole, and has only one value. For example, the number of refrigerators with “freezer on top” produced so far during the model run would be implemented as an Information Store.
2. Changeover & Downtime
Although changeover time and downtime are both non-value-added time, they should be modeled separately and differently. Downtime is related to quality and durability of equipment, and to the care with which the equipment is maintained. Changeover time is related to lot sizes – larger lot sizes reduce changeover time but increase inventory problems (overstock of model A and stockout of model B). Smaller lot sizes reverse this tradeoff, and simulation modeling and analysis can be highly effective at finding the “sweet spot” for lot size. In SIMUL8®, Activity Properties/Efficiency is the route to downtime specification, whereas Activity Properties/Routing In/Change Over is the route to changeover specification.
3. Various Model Types
Continuing, consider the example of the two models of refrigerators. If a Label “Freezer” is used to specify the location of the freezer, and a machine needs changeover time whenever the incoming refrigerator has “freezer on bottom” whereas the previous refrigerator cycling through the machine had “freezer on top” (or vice versa), at Activity Properties/Routing In/Change Over, the modeler will click the When Label Changes option, click Detail and select “Freezer” from the list of Labels.
4. Understanding Scheduled Maintenance
SIMUL8® can also distinguish between downtime (characteristically unplanned and occurring stochastically) and scheduled maintenance (planned for specific times). Clicking on the Data and Rules toolbar, then Scheduled Maintenance, allows specification of scheduled maintenance (start time, end time, repetition interval, and Resources (e.g., workers, special equipment) required. Only one Resource can be thus specified as required. However, Visual Logic can be set to run when the scheduled maintenance begins and when it ends; that Visual Logic could be used to require and free additional Resources. A seemingly open issue: If an Activity (e.g., a machine) is down when scheduled maintenance is to begin, how does SIMUL8® handle the situation?
5. Get familiar with The Resource Matrix Tool
The Resource Matrix is accessible from the Data and Rules toolbar. It appears as a matrix with Activities as the rows and Resources as the column. The modeler can then specify, for example, that the Xray Activity requires one Technician. The modeler is allowed to enter, for example, “1-3” in this matrix – either 1 or 2 Technicians will be required. Open question raised with SIMUL8® — if the modeler enters “1-3”, will 1, 2, or 3 Technicians be required, with probability ⅓ for each possibility? Or some other method of choosing?
Raid Al-Aomar, Edward J.Williams and Onur M. Ülgen.
Understanding The Role of Simulation Modeling
After understanding the concepts and aspects of the term “simulation modeling,” it is necessary to clarify the role that simulation plays in developing production and business systems. Initially, consider the use of simulation technically and economically and then present the spectrum of simulation modeling applications in manufacturing and service sectors.
“Why and when to simulate?” and “How can we justify a simulation project?” are key questions that often cross the mind of simulation practitioners, engineers, and decision-makers. We turn to simulation because of simulation’s capabilities that are unique and powerful in system representation and performance estimation under real-world conditions. Most real-world processes in production and business systems are complex, stochastic, and highly nonlinear and dynamic. Other modeling types such as graphical, mathematical, and physical models fall short in providing a cost-effective and usable system representation under such conditions.
“Decision support” is another common justification of simulation studies. Obviously, engineers and managers want to make the best decisions possible, especially when encountering critical stages of design, expansion, or improvement projects. Simulation studies may reveal insurmountable problems and save cost, effort, and time. They reduce the cost of wrong capital commitments, reduce investments risk, increase design efficiency, and improve the overall system performance.
Although simulation studies might be costly and time-consuming in some cases, the benefits and savings obtained from such studies often recover the simulation cost and avoid much larger costs. Simulation costs are typically the initial simulation software and computer cost, yearly maintenance and upgrade cost, training cost, engineering time cost, and other costs for traveling, preparing presentations with multimedia tools, and so on. Such costs are often recovered through the long-term savings from increasing productivity and efficiency.
A better answer to the question “why simulate?” can be reached by exploring the wide spectrum of simulation applications to various aspects of business, science, and technology. This spectrum starts by designing queuing systems and extends to designing communication networks, production systems, and business operations. Simulation models of manufacturing systems can be used for many objectives including:
Determining throughput capability of a manufacturing cell, an assembly line, or a production system.
Configuring labor resources in an intensive assembly process.
Determining the size and resources in a complex automated storage and retrieval system (AS/RS).
Determining best ordering policies for an inventory control system.
Validating the outcomes of material requirement planning (MRP).
Determining buffer sizes for work-in-progress (WIP) in an assembly line.
For business operations, simulation models can be also used for a wide range of applications including:
Determining the number of bank tellers that results in reducing customers waiting time by a certain percentage.
Designing distribution and transportation networks to improve the performance of logistic and supply chains.
Analyzing the financial portfolio of a company over time.
Designing the operating policies in a fast food restaurant to reduce customer Time-In-System and increase customer satisfaction.
Evaluating hardware and software requirements for a computer network.
Scheduling the working pattern of the medical staff in an emergency room (ER) to reduce patients’ waiting time.
Testing the feasibility of different product development processes and evaluating their impact on the company’s budget and strategy.
Designing communication systems and data transfer protocols.
Designing traffic control systems.
Table 1.1 below shows a summary of ten examples of simulation applications in both manufacturing and service sectors.
To reach the goals of the simulation study, certain elements of each simulated system often become the focus of the simulation model. Modeling and tracking such elements provide attributes and statistics necessary to design, improve, and optimize the underlying system performance. Table 1.2 shows a summary of ten examples of simulated systems with examples of principal model elements.
Like any other engineering tool, simulation has limitations. Such limitations should be realized by practitioners and should not discourage analysts and decision-makers from using simulation. Knowing the limitations of simulation should emphasize using it wisely and should motivate the user to develop creative methods and establish the correct assumptions in order to benefit from the powerful simulation capabilities. Still, however, certain precautions should be considered to avoid the potential pitfalls of simulation studies. We should pay attention to the following issues when considering simulation:
The simulation analyst as well as the decision-maker should be able to answer the question “when not to simulate?” Simulation studies may not be used for solving problems of relative simplicity. Such problems can be solved using engineering analysis, common sense, or mathematical models.
The cost and time of simulation should be considered and planned well. Many simulation studies are underestimated in terms of time and cost. Some decision-makers think of simulation as model building although it consumes less time and cost when compared to data collection and output analysis.
The skill and knowledge of the simulation analyst need to be addressed. Essential skills for simulation practitioners include systems thinking, fluency in programming and simulation software, knowledge in statistics, strong communication and analytical skills, project management (PM) skills, ability to work in teams, and creativity in design and problem-solving.
Expectations from the simulation study should be realistic and not exaggerated. A lot of professionals think of simulation as a “crystal ball” through which they can predict and optimize system behavior. It should be clear that simulation models by themselves are not system optimizers. They are flexible experimental platforms that facilitate planning, what-if analysis, statistical analyses, experimental design, and optimization.
The time frame of the simulation project needs to be realistic and properly set. Insufficient time and resources at various project stages, improper work breakdown structure, and lack of project control are issues that result in project delays and low-quality deliverables. Typical PM skills are essential to execute the simulation project in an efficient manner.
The results obtained from simulation models are as good as the model data inputs, assumptions, and logical design. The commonly used phrase of “garbage-in-garbage-out (GIGO)” is very much applicable to simulation studies. Hence, special attention should be paid to data inputs selection, filtering, and simulation assumptions.
The analyst should pay attention to the level of detail incorporated into the model. Some study objectives can be reached with macro-level modeling while some others require micro-level modeling. The analyst should decide on the proper level of model detail and avoid details that are irrelevant to simulation objectives.
Model verification and validation is not a trivial task. As will be discussed later, model verification aims at making sure that the model behaves according to intended model logic. Model validation, on the other hand, focuses on making sure that the model behaves as the actual system. Both practices determine the degree of model reliability and usefulness.
The results of simulation can be easily misinterpreted. Hence, the analyst should concentrate the effort on collecting reliable results from the model through proper settings of run controls and by using the proper statistical analyses. Typical mistakes in interpreting simulation results include relying on short run time, including biases caused by model initial conditions in the results, using the results of only one simulation replication, and relying on the mean of the response while ignoring variability inherent in response.
The analyst should pay attention to communicating simulation inputs and outputs clearly and correctly to all parties of the simulation study. Also, the results of the simulation model should be communicated to get feedback from parties on relevancy and accuracy of the results.
The analyst should avoid using wrong measures of performance when building and analyzing the model results. Such measures should represent the kind of information required for the analyst and the decision-maker to draw conclusions and inferences on model behavior.
The analyst should also avoid the misuse of model animation. In fact, animation is an important simulation capability that provides engineers and decision-makers with a valuable tool of system visualization. Such capability is also useful for model debugging, verification, and validation. However, some may misuse model animation by relying solely on observing the model for short-term, which may not necessarily reflect its long-term behavior.
Finally, the analyst should select the appropriate simulation software tool that is capable of modeling the underlying system and providing the required simulation results. Criteria for selecting the proper simulation software tool typically include price, modeling capabilities, learning curve, animation, produced reports, input modeling, output analysis, and add-in modules. Simulation packages vary in their capabilities and inclusiveness of different modeling systems and techniques such MHS, human modeling, statistical tools, animation.
A manufacturing plant with machines, people, transport devices, conveyor belts, and storage place.
A bank or other personal-service operation, with different kinds of customers, servers, and facilities like teller windows, automated teller machines (ATMs), loan desks, and safety deposit boxes.
An IT organization with software products, developers (e.g., coders, testers, reviewers, etc), file servers, automated testing tools, software migrations and releases.
A distribution network of plants, warehouses, and transportation links.
An emergency facility in a hospital, including personnel, rooms, equipment, supplies, and patient transport.
A field service operation for appliances or office equipment, with potential customers scattered across a geographic area, service technicians with different qualifications, trucks with different parts and tools, and a central depot and dispatch center.
A computer network with servers, clients, disk drives, tape drives, printer, networking capabilities, and operators.
Freeway system or road segments, interchanges, controls, and traffic.
A central insurance claims office where a lot of paperwork is received, reviewed, copied, filed, and mailed by people and machines.
A chemical products plant with storage tanks, pipelines, reactor vessels, and railway tanker cars in which to ship the finished product.
A fast-food restaurant with workers of different types, customers, equipment, and supplies.
A supermarket with inventory control, checkout, and customer service.
A theme park with rides, stores, restaurants, workers, guests, and parking lots.
Pedestrian flow in malls, museums, buildings, stadiums, airports, plants, etc.
Military planes, rockets, etc. that can be operational at any one time under different scenarios, maintenance, material handling, and supply chain operations.
Raid Al-Aomar, Edward J.Williams and Onur M. Ülgen.
So, What Exactly Is Simulation Modeling?
Simulation Modeling is the art and science of capturing the functionality and the relevant characteristics of real-world systems. Modeling involves presenting such systems in a form that provides sufficient knowledge and facilitates system analyses and improvement. Physical, graphical, mathematical, and computer models are the major types of models developed for different purposes and applications.
This blog posts focuses on defining the simulation concept, developing a taxonomy of different types of simulation models, and explaining the role of simulation in planning, designing, and improving the performance of business and production systems.
Simulation is a widely used term in reference to computer models that represent physical systems (products or processes). It provides a simplified representation that captures important operational features of a real system. For example, FEA represents the mathematical basis for a camshaft product simulation. Similarly, production flow, scheduling rules, and operating pattern represent the logical basis for developing a plant process model.
System simulation model is the computer mimicking of the complex, stochastic, and dynamic operation of a real-world system (including inputs, elements, logic, controls, and outputs). Examples of system simulation models include mimicking the day-to-day operation of a bank, the production flow in an assembly line, or the departure/arrival schedule in an airport. As an alternative to impractical mathematical models or costly physical prototypes, computer simulation has made it possible to model and analyze real-world systems.
As shown in the figure below, the primary requirements for simulation are: a system to be simulated, a simulation analyst, a computer system, and simulation software. The analyst has a pivotal role in the simulation process. He or she is responsible for understanding the real-world system (inputs, elements, logic, and outputs), developing a conceptual model, and collecting pertinent data. The analyst then operates the computer system and uses the simulation software to build, validate, and verify the system simulation model. Finally, the analyst analyzes simulation results and determines best process setting.
Computer system provides the hardware and software tools required to operate and run the simulation model. The simulation software or language provides the platform and environment that facilitates model building, testing, debugging, and running. The simulation analyst utilizes the simulation software on a capable computer system to develop a system simulation model that can be used as a practical (close-to-reality) representation of the actual system.
Based on the selected internal representation scheme, simulation models can be discrete, continuous, or combined. DES models, which are the focus of this book, are the most common among simulation types. DES models are based on a discrete internal representation of model variables (variables that change their state at discrete points in time). In general, discrete simulation models focus on modeling discrete variables that receive values from random or probabilistic distributions, where the state of the system changes in discrete points in time. A discrete variable can be the number of customers in a bank, products and components in an assembly process, or cars in a drive-through restaurant.
Continuous simulation models, on the other hand, focus on continuous variables, receiving values from random or probabilistic distributions, where the state of the system changes continuously. Examples of continuous variables include waiting time, level of water behind a dam, and fluids flow in chemical processes and distribution pipes. Continuous simulation is less popular than discrete simulation since the majority of production and business systems are modeled using discrete random variables (customers, units, entities, orders, etc.).
Combined simulation models include both discrete and continuous elements in the model. For example, separate (discrete) fluid containers arrive to a chemical process where fluids are poured into a reservoir to be processed in a continuous manner. This kind of simulation requires the capability to define and track both discrete and continuous variables.
Furthermore, models are either deterministic or stochastic. A stochastic process is modeled using probabilistic models. Examples of stochastic models include customers arriving to a bank, servicing customers, and equipment failure. In these examples, the random variable can be the inter-arrival time, the service or processing time, and equipment time to failure (TTF), respectively.
Deterministic models, on the other hand, involve no random or probabilistic variables in their processes. Examples include modeling fixed cycle time operations in an automated system or modeling the scheduled arrivals to a clinic. The majority of real-world operations are probabilistic. Hence most simulation studies involve random generation and sampling from theoretical or empirical probability distributions to model random system variables. Variability in model inputs leads to variability in model outputs. As shown in the figure below, a deterministic model Y = f(X) will generate a stochastic response (Y) when model inputs (X1, X2, and X3) are stochastic. If the response represents the productivity of a production system, model inputs such as parts arrival rates, demand forecast, and model mix generate a variable production rate.
Finally, and based on the nature of model evolvement with time, models can be static or dynamic. As shown in the figure below, a simulation model can involve both static and dynamic responses. In static models, system state (defined in state variables) does not change over time. For example, a static variable (X1) can be a fixed number of workers in an assembly line, which does not change with time. Alternatively, a dynamic variable (X2) can be the number of units in a buffer, which changes dynamically over time. Monte Carlo Simulation models are time independent (static) models that deal with a system of fixed state. In such spreadsheet-like models, certain variable values change based on random distributions and performance measure are evaluated per such changes without considering the timing and the dynamics of such changes. Most operational models are, however, dynamic. System state variables often change with time and the interactions that result from such dynamic changes do impact the system behavior.
Dynamic simulation models are further divided into terminating and nonterminating models based on run time. Terminating models are stopped by a certain natural event such as the number of items processed or reaching a certain condition. For example, a bank model stops at the end of the day and a workshop model stops when finishing all tasks in a certain order. These models are impacted by initial conditions (system status at the start). Nonterminating models, on the other hand, can run continuously making the impact of initialization negligible. For example, a plant runs in continuous mode where production starts every shift without emptying the system. The run time for such models is often determined statistically to obtain a steady-state response. The figure below presents a simulation taxonomy with highlighted attributes of DES (discrete, stochastic, and dynamic models of terminating or nonterminating response).
Simulation is a process of using a computer model that represents an existing or planned system to understand the various interactions and constraints in a production system. Simulation modeling allows proposed changes (what-if scenarios) to be tested, productivity, labor and equipment impact analysis of these changes to be performed and understand and visualize the effects of change and the resulting costs prior to implementation.
Production & assembly lines can be a simple or a complex process depending upon the products, the part routing, and facility layout. We put together a list of some commonly received questions for improving processes of all sizes:
Where are my constraints in the process and how to manage them?
How should I allocate my operators?
Where and how much buffer should be added?
Simulation can help identify maximum production line capacity. If the simulation predicted capacity is less than the target, a Throughput Improvement Road Map (TRIM) as shown in Figure 1 can be developed using Simulation studies to achieve the target capacity. The TRIM will identify the constraints (might be cycle time, station/equipment downtime, changeovers, etc.) that will have the most impact on the production. The Team can then and decide the best way to bust the constraints based on cost, ease of implementation, resources required, etc.
Figure 1 – Throughput Improvement Road Map (TRIM)
Simulation can also be used to validate operator or resource allocations. Initially, line balancing technique can also be used to smooth out the production flow by allocating task to the operators such that the task can be completed within the allocated time. Line balancing data then can be fed into simulation for validating the output. What-if scenarios with different operator allocation or schemes can be run to see impact on throughput and overall operator utilization as shown in Figure 2, thus reducing overall cost.
Figure 2 – Operator Relocation Analysis
There is always a tendency to add more buffer to the production or assembly process. Adding buffer is not cheap, buffer not only increase inventory cost but also require additional capital to buy equipment to store and move the product. Example – A conveyor needs to be extended to accommodate more buffer, additional fixtures required to hold a product in a automated robotic process etc. Simulation can not only help identify location of the buffer but also quantity of buffer required. Buffer sensitivity analysis as shown in Figure 3, can be conducted to identify number of buffers required at a certain location.
Figure 3 – Buffer Sensitivity Analysis
As you can see Simulation can offer various insights in identifying the constraints, use of labor and identifying location and quantity of buffers, thus simulation can not only help increase production but also help in reducing the cost, thus increasing profits!