The Simulation Series, Part III:
Discrete Event Simulation
In Part I of this series, we discussed the importance and benefits of using simulation technology in the manufacturing industry.
In Part II, we explored the various types of simulations used in manufacturing.
In Part III, we now examine why Discrete Event Simulation (DES), which has been used since the inception of computers in the 1950s, stands out as one of the most popular simulation analysis methodologies.
What is Discrete Event Simulation?
DES is a dynamic, stochastic, discrete, and individual-based simulation analysis methodology. It models the operation of a system as a sequence of events in time. Each event occurs at a particular instant in time and marks a change of state in the system.
Most DES platforms operate based on a technique called "next-event." This means the model is only examined and updated when a state change is known to occur. In short, the simulation clock moves from one event to the next.
In general, simulation capabilities have continuously improved as computing power has increased. Dynamic data exchange, object linking and embedding, and multi-tasking operating systems have further pushed simulation model integration with other software applications. The most common example is with Microsoft Excel: a DES model takes inputs, processes the inputs for time period as per the system’s behavior, and produce outputs as results of the simulation analysis.
Microsoft Excel provides a user-friendly interface for data input and reporting results. A DES model can also interact with advanced database and industrial systems to fetch real time data. DES simulation models are being integrated with other simulation methodologies, such as system dynamics and agent-based simulations, to holistically simulate various dynamics within industries and businesses.
When to Use Discrete Event Simulation
A quickly changing marketplace continues to increase the complexity of challenges faced by industries, whether they are manufacturing, Fast-Moving Consumer Goods (FMCG), processing, inventory, warehouse, logistics, supply chain or healthcare services. Meanwhile, the lead time available to bring new products and services to market is shrinking. Both greenfield and brownfield projects are challenged to cope with market dynamics and introduce and produce products as fast as possible with minimal cost. Even introducing one new variant increases the complexity of procurement, storage, processing, logistics and more across the supply chain. That, in turn, can lead to uncertainty of deliverables and throughput. DES analysis is the answer to these challenges.
Benefits of Discrete Event Simulation for Industries
- Reduced uncertainty
- Insights into complex dynamics
- Increased throughput
- Decreased parts time in the system
- Reduced in-process inventories of parts
- Increased utilizations of resources
- Increased on-time deliveries of products to customers
- Reduced capital requirements and/or operating expenses
- Experimentation and optimization with nominal capital cost
- Insurance that a proposed system design will operate as expected
Operation research scientists are researching in collaboration with information technologist to further enhance Parallel Discrete Event Simulation (PDES), along with simulation model approaches, experimental design and optimization, human performance modeling, and healthcare modeling. These efforts will further increase the capability to simulate large-scale and complex simulations using DES.
Part IV of The Simulation Series will address the history and utilization of Robotics Simulation in manufacturing.
In Part V, we'll look at Emulation as a way of virtually commissioning industrial systems.
References:Manufacturing Systems: Foundations of World-Class Practice (1992), National Academy of Engineering
Simulation Modeling and Analysis, Averill M. Law & Associates Inc, McGraw-Hill Education
Practical Reliability Engineering, John Wiley & Sons Ltd.