In manufacturing, every minute spent in idle time, queuing or in inefficient resource usage, affects throughput, quality and profitability. Discrete Event Simulation (DES) is a powerful technique that models how a process unfolds over time by tracking individual "events", such as for instance the arrival of materials, the start of a batch or a machine breakdown. Unlike standard or continuous simulations that track variables at every moment, DES jumps from one event to the next and so this method is especially well-suited for systems that involve waiting lines, scheduling decisions, resource competition, or shift patterns.

With DES you can identify bottlenecks by understanding where queues build up and why, you can conduct scenario testing by comparing different layouts, staffing models or batch schedules, you can carry out downtime analysis to assess the ripple effects of delays or failures, and you can support decisions to provide data-backed insights for capital investments or process change. 

For industries with variable demand, perishability, and complex multi-step processes, such as food, pharma and logistics, DES is truly transformative. 

Let's say we have a production line. DES would break the system down into discrete events such as the product entering a station, a machine starting/stopping, a worker becoming available, a batch changeover, a conveyor breakdown and a waiting queue forming. Each event changes the state of the system and happens at a particular point in time. 

Take for instance yoghurt production, where you have the filling station where tubs get filled, the sealing machine, the labelling machine and the packing station. After you have set up the logic, you can then simulate the arrival rate of yoghurt tubs, the processing time per machine, the downtime or maintenance events, operator availability, queues forming between stations and bottlenecks or underused equipment. The questions that Discrete Event Simulation helps you to answer are "What happens if I add another labelling machine?", "How much is the downtime before output drops by 10%?", "What if operator shifts are staggered?", "Is packing keeping up with filling?"...

Basically, you can answer these questions without trial and error. It can help with decision making when a company is, for instance, trying to assess whether it would make sense to purchase another machine. Since with DES you can simulate it in a virtual environment and compare the current set-up to the proposed new setup, it helps you measure the impact of the overall output, the utilisation of the new machine, the changes to queue lengths or waiting times, the ROI from improved throughput and whether adding a machine just shifts the bottleneck elsewhere. Bottom line is that DES helps you make data-backed investment decisions by showing whether the performance gain justifies the cost, before you spend a cent.