Supply Chain Simulation vs. Optimization

These days, optimization and simulations are building blocks of supply chain planning. Both frameworks have a lot to offer, but they are also fundamentally different. Knowing why and when to use one or the other (or both) will ensure your company harnesses these powerful digital tools for all that they’re worth.

Both optimization and simulation involve computer-driven, data heavy processes: in other words, a whole lot of number crunching and analyzing the interplay of complex variables. They are used across a range of sectors to problem solve and plan for the future, short- and long-term. Simulation and optimization are utilized by medical, manufacturing, energy, and transportation industries, playing an integral role in SCP. 

As you familiarize yourself with supply chain terminology, it might be easy to fall into using these terms interchangeably or mistaking one for the other. Avoid this by delineating the differences between them early on. This way, you can recognize when your supply network can benefit from leveling up and choose the technique that delivers the kind of information you need to move ahead.

Simulation

The term “simulation” is familiar to most of us outside of a supply chain context. We get the idea: a true-to-life replica, a model, a parallel reality that allows us to test something without actually experiencing the real life version. Simulations fuel progress in disciplines as diverse as aerospace engineering to biology to sociology. 

So where do they fit into supply chain planning?

Simulation allows supply chain managers to model entire systems by varying input data to explore how such changes impact output data. By employing simulation, we can conduct what-if scenario analysis even when a high degree of uncertainty or ambiguity exists. 

With simulation, inputs can be dynamic and variable — a range rather than a set value. Rules about the system are developed, but then input data (say, demand for a product) can shift up and down. How do other components of the system fare when demand changes? That’s just one key piece of information we can take away after running a supply chain simulation.

Simulations are built to be probabilistic. We run them when we want to evaluate predetermined options using a series of realistic scenarios, with an eye towards exploration rather than a one-size-fits-all answer. This type of modeling gives us hard data that can then better inform our long-term decision-making. The computer doesn’t make a decision for your team; it just gives you the insight to guide you towards the most informed choice. 

The strategic time frame is the perspective we’re operating in when we think about simulation. Average flows and average volumes — rather than fixed quantities — are the bread and butter of simulations. The level of granularity here can be months, quarters, or years ahead. We’re observing the way the supply chain reacts to adjustments to inputs over time. 

While simulation won’t identify a single answer to a determined problem, it can shed light on bottlenecks, give us the opportunity to draw up hypotheses, and then alter variables (e.g. increasing supply) to visualize the impact. The ultimate test drive. 

Minimizing risk, building resilience, understanding the supply network holistically, predicting consequences — these are just a few of the reasons why supply chain managers turn to simulations.

Optimization

Most of us prefer certainty and clearly defined solutions, and there are times when we simply need to operate within a deterministic framework. That’s where optimization shines. Its aim is to serve up a scientifically based “best” answer.” 

In supply chain planning, optimization demands fixed variables and known relationships between factors (for example, revenue, manufacturing plants costs, and shipping tariffs). When we have this data, optimization can deliver parameters that maximize or minimize a target variable, like profit or surplus inventory.  

Optimization takes into account constraints that are tied to achieving a specific target. It’s utilized most effectively when we have a high degree of clarity about these constraints and the relationships between each variable. Scheduling, inventory management, transportation flow — these are just a few of the realms in which optimization methods can be applied to arrive at a distinct, efficient solution. 

As far as SCP is concerned, optimization is a “black box” method. That means the computer’s algorithm offers a solution to a problem, and once it’s in the managers’ hands, they have to trust it — even when it conflicts with their preconceived judgements or intuition. If things go wrong, this can create significant conflict. Given its “black box” nature, optimization is also not as skilled at looking at the supply chain as a whole and the complex dynamics at work within it. 

Optimization fits well within the operational time frame since input data and parameters need to be well known. Typically the further ahead we look, the fuzzier those parameters may be, so it is best enlisted in shorter-term decision-making. 

Where Do They Diverge?

We’ve explored simulation and optimization under the microscope. Now it’s time to fully examine their critical differences. The key is in understanding the strengths and weaknesses of both approaches, and ideally, understanding which types of scenarios call for using one or both. 

One right answer vs. Valuable insight

Are you searching for a single, precise solution to a complex problem? That’s where optimization excels. When supply chain managers are seeking insight to drive a decision about a dynamic system, simulation is a better fit. Although optimization is a “black box,” it points towards an answer. Simulation doesn’t give you an answer — it gives you data. It’s then up to the supply chain manager to chart the best course forward given that information.

Fixed vs. Random Parameters

Optimization requires relationships between variables to be clearly defined. Not so with simulation. With the latter approach, parameters can hover around an average. Simulation is built to handle random variations, while optimization demands greater precision.

Known vs. Variable Constraints

Similar to parameters, constraints are also clear when utilizing optimization as an approach. Imagine a transportation fleet that has access to a fixed number of trucks. That number is static. With simulation, constraints vary within realistic ranges rather than holding static at a predetermined cap.

Tactical & Strategic Solutions Vs. Long Term Strategy

It’s all in the name. Optimization is about finding an optimal solution; the one, clear answer that maximizes or minimizes your organization’s target, whether that’s profit, risk, or something else. Efficiency is the driver. Answering the question and solving the defined problem is the goal. Simulation, by contrast, focuses on collecting observations about the supply chain’s behavior during a range of different circumstances to develop strong decision-making strategies. 

Some scenarios can benefit from both optimization and simulation techniques, so while it’s fundamental to understand their differences, it’s equally important to recognize that neither approach has to stand alone. 

Ready to learn more about simulation, optimization, and supply chain management in an ever-uncertain world? Then you simply can’t afford to miss Patrick Rigoni’s next webinar.