Process modelling tells you how something works. It focuses on what happens such as unit operations, material transformations, mass and energy balances and quality parameters. 

Discrete Event Simulation (DES), on the other hand tells you when, how often and for how long it works (or doesn't). DES focuses on when it happens and looks at things such as queue times, delays, capacity utilisation, operator shifts and schedule adherence. 

So, on their own, each of the methods offer insight, but combined they create a more complete picture of dynamic systems in food and manufacturing operations. This is because you can simulate not only the steps in your process but the timing and resource implications under different conditions. Some examples are when simulating a bottling line where temperature profiles (process model) interact with shift patterns and downtimes (DES) or when modelling a bakery's dough fermentation (process kinetics) alongside batch scheduling and equipment cleaning cycles. It can also help evaluate how small changes in batch size affect overall equipment effectiveness, energy use and delivery times. 

This hybrid approach is increasingly useful for digital twins, capacity expansion planning and investment decisions, especially in food, where shelf life, throughput and variability make timing critical. Insight emerges not just from what happens but from when it happens and to what extent and that is where process models and DES complement each other. 

Let's take the example of a biscuit production line where we want to improve throughput while reducing energy use and bottlenecks. The current situation is that you have a production line with dough mixing, baking, cooling and packaging stages. The oven is a bottleneck, running at maximum capacity, and so packaging machines are often idle waiting for a product and as a result energy use spikes due to a "stop-start" behaviour. 

So, DES in this case simulates the line and models every step including the details such as batch sizes, machine speeds, delays and shifts. It shows where and when queues form, machines go idle, or capacity is underused, and it tracks performance metrics like overall equipment effectiveness, throughput and idle time.

Process optimisation on the other hand, when applied, adjusts batch size, buffer sizes and timing to reduce idle time, it tests different staff schedules or machine speeds and it identifies oven usage patterns that smooth out energy consumption. The result may be increased throughput without new equipment, reducing packaging downtime, lower energy costs by optimising baking cycles and data-backed confidence in changes before implementing on-site.