Discrete-event simulation (DES) can be applied to a wide range of industries and domains where events occur in a discrete, sequential manner. It is applied when a system is so complex that it cannot be understood or solved with analytical methods. Complex systems are systems with many interdependencies and dynamic behaviour. DES is not the only viable method for analyzing and designing such systems, but DES is especially effective and popular for systems that can be described with queues and sequential processes. E.g. production processes, business processes, etc.

Commercial applications are widely known in industries such as manufacturing, transportation, healthcare, services (e.g. banking transactions, call centers), as well as military, defense and social sciences. In today’s blog post I will point out some reasons for why discrete-event simulation has been applied in these industries and domains for decades. I will summarize case studies that I came across in the past and highlight key points of interest to investors and sponsors signing of on major investments.

DES case studies highlighting its economic benefit

I have come across many case studies confirming the economic and/or strategic benefit of DES.

For example, a DES model was used to evaluate the impact of implementing lean manufacturing techniques in a production line. The model helped identify bottlenecks in the production line. The simulation model and simulation study furthermore indicated several inefficiencies in the line. Resulting process improvements contributed to a 71% reduction in lead times, a 30% reduction in inventory capital (semi-finished product in buffers), and a 18% reduction in required capital for production equipment (molding machines, assembly cells and tooling).

Another exemplary case study that I can point out is related to supply chain management: A DES model was used to optimize the supply chain of a OEM-supplying manufacturing company. The model simulated the flow of materials, products, and information through the supply chain, and helped identify opportunities for improved ordering and inventory policies, resulting in elimination of backlog built-up.

Other case studies, many of them already published on this blog, e.g. include better planning of open-pit mining operations (or open-cast mining), factory layout design in chemical process industry, as well as e.g. hands-on projects such as a parking lot capacity utilization simulator.

DES projects require investments but generate returns

A DES project is costly but can result in even bigger savings. Such savings result from a steeper learning curve (learning by simulating instead of learning by doing) and improved planning. Layout improvements, capacity adjustments, policy adjustments, production planning – all of this aspects can be improved early on by the insights generated from DES projects. If you are planning a 1 billion USD project you might be interested in investing 200,000 USD into DES modelling and simulation if that means your system can be improved so that you now achieve greater performance with less invested capital.

Risk reduction is a major motivation behind most DES projects. For example, DES is usually applied to investment projects that require high amounts of invested capital. DES might even be required for sign-off on projects with high investment need. Simulation studies are used to design and simulate the system before it is actually built. This allows to run what-if scenarioes, but also functions as a proof of concept in general.

Why most DES projects fail

Without clear goals and objectives, it may be difficult for the simulation department to prioritize projects, allocate resources, and demonstrate the value of their work to senior management. But even if goals and objectivs are clearly defined effective DES application and implementation is not guaranteed. Clear goals and objectives are just the first step of any serious DES procedure model. Choosing an appropriate procedure model for DES application or implementation is critical. The procedure model applied by me for simulation study execution is displayed below.

Other common reasons for why DES projects fail are:

• Insufficient investment in technology and software
• Inadequate staffing and expertise
• Lack of collaboration with other departments
• Failed change management
• Inability to demonstrate economic value
• Poor quality of available data

You should learn from these mistakes and avoid making them in the first place.

Concluding remarks and related content

DES is only one of several simulation methods being used in business and management. I have already introduced all major relevant simulation methods for SCM analysts on this blog. For example, agent-based simulation can also be used for supply chain and production line simulation. You should familiarize yourself with these methods first. DES is especially interesting for sequential processes and queues. You can learn more about DES, its applications, and also other simulation methods on this blog. Some blog posts that you should consider reading are:

• Link: Discrete-event simulation software list
• Link: Open-cast mine simulation for better planning
• Link: Visual Components financial KPI simulation
• Link: End-to-end poultry supply chain simulation
• Link: Parking lot simulator with simmer in R
• Link: Manufacturing simulation for plant design

Linnart Felkl

Data scientist focusing on simulation, optimization and modeling in R, SQL, VBA and Python