Importance of Simulation in Agribusiness - Agribusiness Education and Research International

Importance of Simulation in Agribusiness

Introduction:

Simulating the operation of a real-world process or system over time is known as simulation. The importance of Simulation in Agribusiness has some contribution in the context of the 21st century. Simulation is creating an artificial history of the system and observing that history in order to make conclusions about the operating characteristics of the real system being mimicked. For the solution of many real-world problems, simulation is an essential problem-solving methodology. Simulation is used to explain and analyze system behavior, pose what-if questions regarding real-world systems, and aid in the design of real-world systems. Simulation can be used to model both real and hypothetical systems.

Some Examples of Simulation:

Simulation is widely employed in the automobile industry. Radars and sensors that detect the movement of various barriers around the car are now standard in modern automobiles. Before any technology is applied to all cars, it is first tested in a simulation, and then if the testing is successful, the technology is integrated into the car.
If a corporation wants to figure out how many employees it will need to handle its customers, it can use simulation. In the simulation, a computer-generated mockup of the company will be created. The computer then creates all of the entities, such as clients, employees, and other firm members. The number of clients, employees, and time have all been established to put the company’s performance to the test. All simulation and animation work will be done by computer software.
Other types of simulation include character simulations in movies, video games, animation videos, television commercials, and climate change simulations. All of these simulations necessitate a powerful computer. If you want to test a computer simulation of a battle between countries, you’ll need a supercomputer. The supercomputer will mimic the war and calculate the number of lives lost and the amount of damage caused.

Some Real-Life Examples of Simulation:

Simulation allows testing technologies at a cheap cost. Because testing on a real thing might be risky at times, we utilize simulation to test the behavior of an object in various settings.

Simulation is also used in the stock market. To purchase and sell stocks on the stock exchange, each stock brokerage firm has its own software. We can also simulate profit and loss of shares using that software. Customers who are new to the company are provided access to the software as well as virtual cash to test it. This is accomplished through simulation, which involves mimicking the sale or purchase of actual stocks. However, it is only for the purpose of training or experimentation.
Doctors also utilize simulation to test different drugs and diagnose patients with various ailments. This is mostly accomplished through the use of a computer screen and a small device placed into the patient’s body. Because computers are employed in almost every element of our lives, they make our lives easier. They are also incredibly beneficial for replicating and testing various aspects of our lives.
Real airplanes are not handed to new pilots to fly. They are given a simulated plane that does not exist at the beginning. The simulated airplane features a screen that looks exactly like a real airplane’s screen, and all of the controls are identical. After the new pilot has completed his training, he is given the authorization to pilot an actual airplane.

Classification of simulation:

Stochastic vs. deterministic

If the behavior of a model is completely predicted, it is said to be deterministic. The model will provide a unique set of outputs given a set of inputs. A model is stochastic if the inputs are random variables, and the outputs are also random.

Consider the case of the donut business. In a deterministic model, we can assume that a new customer arrives every 5 minutes and that an employee serves a customer in 2 minutes. In a stochastic model, however, we would suppose that the arrival and serving times are determined by random variables, such as normal distributions with mean and variance parameters.

Static vs. dynamic

Static simulation models are those that simply depict the system at a single moment in time. Monte Carlo simulations are a sort of simulation that will be discussed in more detail in later chapters.

Dynamic simulation models show how systems change throughout time. A dynamic model is an example of a simulation of a donut store during business hours.

Continuous vs. discrete simulations

Discrete and continuous dynamic simulations are two types of dynamic simulations.

The variables of interest in discrete simulation models change only at a discrete selection of points in time. A discrete simulation is an illustration of the number of people queuing in the donut shop. Only when a new client arrives or when a customer has been served does the number of customers change.

The discrete character of the number of customers queuing in the donut shop is illustrated in Figure 1.1.

Figure 1.1: Example of Discrete Dynamic simulation.

Figure 1.1 further shows that the system does not change state for a certain amount of time, i.e. the number of consumers queuing remains constant. As a result, inspecting the system during times when nothing changes is pointless. This promotes the use of the so-called next-event technique to deal with time in dynamic discrete simulations. When the system is about to change, the model is inspected and modified. These shifts are sometimes referred to as events. In Figure 1.1, an event occurs at time zero: a client enters; another customer arrives at time nine; another customer arrives at time ten; a customer is serviced at time twelve; and so on.

The variables of interest in continuous simulation models change constantly over time. Assume you’ve constructed a simulation model for an automobile ride in which the focus is on the car’s speed throughout the voyage. This would thus be a model of continuous simulation.

Objects of the model

A simulation model is frequently made up of two types of objects:

Entities:

Individual aspects of the system that are being simulated and whose activity is being tracked explicitly are referred to as entities. Each entity can be identified separately.

Resources:

Individual aspects of the system are also called resources, although they are not modeled separately. They’re considered as though they’re countable objects whose activity isn’t monitored.

The modeler must select whether an element should be handled as an entity or a resource, and this decision is based on the simulation’s goal. Take, for example, our humble donut shop. Clients will most likely be used as resources because we aren’t very interested in what they do. Employees can be thought of as either entities or resources: in the former situation, we want to measure how much time each of them spends working, while in the latter scenario, the model can only provide an overview of how active the employees are overall.

Organization of Entities and resources:

Attributes: Attributes are the characteristics of objects (that is entities and resources). This is frequently used to control an object’s behavior. In our doughnut shop, an attribute could be an employee’s availability: whether she is busy or not. An attribute in a more detailed simulation might be the type of donut a consumer will purchase (for instance, chocolate, vanilla or jam).

State: a set of variables that can be used to characterize the system at any point in time. In the simplest scenario, the necessary variables in our donut business are the number of customers queuing and the number of busy employees. This is a complete description of the system.

List: A list is a collection of entities or resources that are arranged in a logical order. Customers waiting at our shop, for example, maybe served according to the “first-come, first-served” principle, which means that they will be served in the order in which they arrived.

Operations of the objects:

Entities and resources will collaborate and so alter state during simulation research. This, as well as the passage of time, is described using the following terminology:

Event: An event occurs when the status of the system changes at a specific point in time. Assume that two customers are currently being served in the donut store. When a client is served, an event occurs: the number of busy staff drops by one, and there is one less person in line.

Activity: a predetermined duration of time that is known when it begins (although its length may be random). An activity is something like how long it takes an employee to service a customer, and it can be expressed in terms of a random distribution.

Types of Simulation Model:

Monte Carlo/Risk Analysis Simulation.
Agent-Based modeling & simulation.
Discrete event simulation.
Systems dynamic simulation solution.
Monte Carlo/Risk Analysis Simulation:

Monte Carlo simulations are used to represent the probability of various outcomes in a process that is difficult to anticipate due to random variables’ intervention. It’s a method for figuring out how risk and uncertainty affect prediction and forecasting models.

A Monte Carlo simulation can be used to solve problems in almost any industry, including finance, engineering, supply chain management, and science. A multiple probability simulation is another name for it.

When faced with high uncertainty in the process of creating a forecast or estimation, the Monte Carlo Simulation may prove to be a superior solution by using numerous values rather than just replacing the uncertain variable with a single average figure.

Monte Carlo/Risk Analysis Simulation Applications:

Monte Carlo simulations have a wide range of possible applications in business and finance because these disciplines are plagued by random variables. They are used to predict the possibility of cost overruns in large projects and the likelihood of an asset price moving in a specific direction.

Telecoms utilize them to evaluate network performance in a variety of circumstances, which aids in network optimization. They’re used by analysts to measure the danger of an organization defaulting and to study derivatives-like options.

They’re also used by insurers and oil well drillers. Outside of business and finance, Monte Carlo simulations have a wide range of applications, including meteorology, astronomy, and particle physics.

Monte Carlo/ Risk Analysis Simulation History:

Because chance and random results are important to the modeling technique, as they are to games like roulette, dice, and slot machines, Monte Carlo simulations are called after the popular gambling site in Monaco.

Stanislaw Ulam, a mathematician who worked on the Manhattan Project, was the first to invent the approach. Ulam kept himself occupied after the war while recovering from brain surgery by playing endless rounds of solitaire. He became fascinated in plotting the results of each of these games in order to observe their distribution and calculate the chances of winning. After sharing his concept with John Von Neumann, the two worked together to create the Monte Carlo simulation.

Method of Monte Carlo Simulation

The likelihood of varying outcomes cannot be estimated due to random variable interference, which is the basis of a Monte Carlo simulation. As a result, a Monte Carlo simulation focuses on repeatedly repeating random samples in order to reach specific outcomes.

A Monte Carlo simulation assigns a random value to the variable that is uncertain. After that, the model is run and a result is provided. This step is repeated numerous times while assigning various values to the variable in question. After the simulation is finished, the data are averaged to get an estimate.

Agent-Based modeling & simulation:

Agent-based modeling and simulation, a method of replicating the behavior of a complex system in which agents interact with one another and with their environment using simple local rules, is gaining popularity and widespread use in a variety of fields. The ability of this method to anticipate traffic flow in urban areas, the spread of contagious diseases, and the behavior of economic systems has piqued interest in this potent technology.

The notion of multiagent systems is applied to the basic structure of simulation models in agent-based modeling and simulation, also known as multiagent simulation or multiagent-based simulation. The term “agent-guided simulation” is also sometimes used to refer to a broader concept.

The ABMS models and implements active components or decision makers as agents, using agent-related principles and technology. As a result, agent-based modeling (ABM) can be characterized as an original or reference representation of a multiagent system. The generic modeling and simulation paradigm is referred to as ABMS in this study, whereas ABM refers to the specialized activity of modeling and ABS refers to the execution of a model.

In order to construct an agent-based model, the following three variables must be addressed explicitly. The set of agents, first and foremost, is the most differentiating trait. In comparison to the other aspects in the virtual world, these agents are self-contained. Following that, the agents’ interactions with one another and with their shared environment are specified. Because these interactions are responsible for the final outcome, the design of all important aspects is critical. Interactions do not need to be represented clearly.

ABMS (agent-based modeling and simulation) has its own set of problems. Furthermore, the application domains in which such technologies are deployed are quite diverse, spanning from software systems to information economies and critical infrastructures. As a result, building a good and broadly applicable theory for such systems will require an inter-disciplinary approach as well as novel mathematics and computational concepts.

Discrete event simulation:

The operation of a system is modeled as a (discrete) series of events in time in a discrete-event simulation (DES). Each event occurs at a specific point in time and represents a change in the system’s state. Because no change in the system is expected between consecutive events, the simulation time can leap immediately to the occurrence time of the next event, which is known as the next-event time progression.

In addition to next-event time progression, there is a fixed-increment time progression strategy, in which time is divided into small time slices and the system state is updated based on the set of events/activities occurring in each slice. A next-event time simulation can often run significantly faster than a matching fixed-increment time simulation since not every time slice needs to be simulated.

Systems dynamic simulation solution:

A System Dynamics Simulation is an abstract modeling technique that is used to depict a system in a broadway. It varies from previous simulation models in that it ignores details about the system’s humans (or machines and interactions).

System Dynamics Simulations are not focused on individual actions, despite the fact that they lack the small details. Instead, they reveal aggregate-level insights coming from an activity.

For example, to utilize a System Dynamics Simulation to see if a new marketing strategy or a bigger marketing budget leads to more sales. A System Dynamics Simulation is built on these feedback loops.

Simulation in Agribusiness Process: A simulation is a valuable tool for testing real-world situations and processes without actually putting them into action. Working with simulations can save time and resources since they let you identify where changes can be made before processes are implemented.

A business process is a collection of interconnected actions and activities that lead to a specified goal or end. Agribusiness process simulation is a tool that may be used to test and assess both existing and unimplemented business processes.

Simulation is used to determine how a process might work in the real world before it is developed. Furthermore, process simulations allow you to be creative and experiment with a range of ideas and situations until you find the ones that work best for you without disrupting current production cycles.

Agribusiness process simulation can also be useful as part of a larger process improvement strategy. Simulating production and operations is a low-cost and low-impact technique to look for ways to improve them.

Livestock systems, as well as agricultural activities in general, are key sources of raw materials for agribusiness. In terms of animals, this industry is heavily reliant on thermal comfort within the facilities, with ventilation systems designed to offer an atmosphere with a uniformly distributed temperature, allowing the animals to achieve maximum production. Similarly, agricultural output necessitates sufficient environmental control, such as greenhouses, and understanding the microclimate can assist farmers in resolving environmental issues. In this application, computational fluid dynamics (CFD) is a recent numerical methodology. In this context, Computational Fluid Dynamics (CFD) is a current numerical technology that may be used to various industries’ projects and analyses.

For performance benefits, 3D computer simulations can be used in a variety of ways.

Following applications:

Analysis of the environment in which animals live, in order to improve productivity.
For better energy efficiency, conduct a thermal distribution analysis.
Temperature, humidity, and airflow behavior are studied to reduce losses.

Importance of simulation in the agribusiness Process:

Process improvement, training, education, testing, safety, and experimentation are all areas where simulations are employed in a number of sectors.

Resource conservation: better it’s to model an agribusiness process and run it as a simulation than to invest time and money building and implementing a process only to discover that it’s wrong. Because it has no influence on present work in agribusiness, finding and addressing problems early in a simulation can save time and money.

Visual output: Agribusiness process models display processes and model designs in an easy-to-read visual format. Running simulations based on BPMN models allows to readily understand the connections between different activities and determine where tasks should be added to or deleted from the process flow. It’s easier to communicate past and future changes in the process to managers and stakeholders when using visual outputs from simulations.

Testing business process: Testing Agribusiness process behavior before its developed provides a fair idea of how it’ll work in practice.

Problem-solving: Observing and analyzing behavior allows seeing what works and what doesn’t. Fixing simulated problems is easier and less expensive than fixing real-world difficulties.

Education & Training: Simulations are a smart, cost-effective approach to give new employees hands-on practice and familiarity with processes and systems while not interfering with genuine, real-time workflows.

Accurate Results: The results of a simulation are usually accurate, and they can assist to know what to expect when moving the process from the virtual to the actual world.

Simulation and forecasting: In most cases, forecasters merely provide a point estimate of a variable. Because probabilistic forecasts are developed and reported using simulation. Risk will be factored into estimates for corporate decision-making. Simply put, this entails presenting more information about the projection than a basic confidence interval. Furthermore, if present a range with a probability about the center point, it is more difficult to be proven wrong.

Five steps to Agribusiness simulation Process:

Step 1: Define the objectives or issue.

It’s unlikely that creating models and simulations is solely for the sake of having fun. There must be a precise cause, such as a procedure that has to be improved or an issue that must be solved.

Determine the type of data to collect and which parts of the process should be modeled and simulated in order to collect it & to do simulations to figure out why client wait times are so long, for example.

Step 2: Create a conceptual model and execute the first-pass simulation.

When creating a model, it may be easier and less time-consuming to proceed from simple to complicate. Make a model of the regions you wish to keep an eye on. Perform a first pass simulation on the model and examine the results. As needed, increase the model’s complexity to guarantee that are obtaining all of the data that need to solve the problem.

Step 3: Check the simulation’s accuracy.

Run a simulation to ensure that it behaves as would expect a real-world process to behave. Make any necessary changes to the model until the simulation matches the real-world process. Keep in mind that the data will need to collect may change model develops or as complexity is added.

Step 4: Analyze the results.

Analyze the simulation’s results. Evaluate the data to see if it matches the expectations. To acquire the results that are desirable, tweak the model and run the simulations as many times as necessary.

Step 5: Share simulation results and implement changes.

Share the outcomes with managers, team members, and other stakeholders after all simulations have been completed and data has been collected and reviewed. Demonstrate the simulations to all interested parties so they can see how new ideas and adjustments can help to improve or correct procedures.

As work to address areas that need improvement, business process simulation is cost-effective, saves time, and has little influence on existing operations. After assessing the results, confidently implement the process without causing too much disruption in the workplace.

Conclusion:

Simulation plays a significant role in Agribusiness. Simulation is a powerful tool for evaluating and analyzing new system designs, existing system alterations, and proposed control system and operating rule changes. A valid simulation is both an art and a science to carry out. The main objective of Agribusiness simulations in the workplace is to improve Agribusiness acumen and provide participants with the skills and information they need to carry out corporate strategic plans to estimate exactly how much product the agricultural company should contract to sell in order to maximize their total profit margins.

Reference:

Banks, J. (1998). Principles of simulation. Handbook of simulation, 12, 3-30.

Klügl, F., &Bazzan, A. L. (2012). Agent-based modeling and simulation. Ai Magazine, 33(3), 29-29.

Retrieve from https://www.lucidchart.com/blog/business-process-simulation

Retrieve from https://bookdown.org/manuele_leonelli/SimBook/types-of-simulations.html

Retrieve from https://www.wikiaccounting.com/simulation-models

Retrieve from https://elearningindustry.com/business-simulations-benefits

Retrieve from https://www.phdata.io/case-studies/food-agribusiness/

Retrieve from https://tauflow.com/en/agribusiness-simulation/

Shanton, K., & Goldman, A. (2010). Simulation theory. Wiley Interdisciplinary Reviews: Cognitive Science, 1(4), 527-538.

Written by Mahamudul Hasan Millat

Research Scholar