Optimization is great for many things but terrible at capturing uncertainty and implementing recommendations on its own. Implementing optimization recommendations still requires a big dose of, “Well, I hope this works!” 

For years, simulation was dismissed because of performance and memory requirements. Nowadays, those barriers are gone, meaning simulations can be completed at the order and shipment level, allowing businesses to calculate true service rates for proposed supply chain states. 

Simulation vs. Optimization 

Back in the 1990s, companies frequently said they wanted to calculate service rates — the percentage of customers that got what they requested by the time they requested it — when modeling network design problems. At the time, powerful tools used to optimize networks couldn’t predict service rate changes. 

Optimization is generally used to define the best network structure and is typically most concerned with cost. Meanwhile, simulation is often used to replicate a system’s performance and to examine the impacts of changing business rules or other elements (customer ordering patterns, processing times, etc.). 

Simulation is the only approach that can accurately predict service rates. LLamasoft was originally founded as a supply chain simulation company to predict the effect of network changes on service levels. It incorporated mathematical optimization and hoped companies would use both tools to evaluate supply chain changes and quantify the impact on service. 

When a traditional network optimization study recommends a facility be shut down, too often, other facilities are left to pick up volume, capacity limits are pushed, warehouse spaces need to be rented and last-mile transport costs need to be elevated. But if that same network study had used simulation, the decision to close the facility likely wouldn’t have been made. 

Inventory Simulation 

Inventory selection — how much to stock and where — has a massive effect on service rates. When customers are shifted to different facilities, variability and transport times are impacted. Traditional network optimization cannot model inventory over time at the order and shipment level. 

Simulation allows operators to quickly identify the effects on inventory as a result of proposed network changes, which cannot be done with deterministic optimization. 

Simulation Synthesizes Future-State Supply Chain Data 

Only recently has simulation become mature and accessible enough to be used by people who are not data scientists. 

Simulation of real-sized supply chains bogged down even the most powerful systems as recently as 23 years ago. To bypass speed and memory constraints, users would unsuccessfully simulate by aggregating into product or customer groups. The right level of detail is individual customers, orders and shipments. 

Simulating supply chains at transactional levels was out of reach until cloud-native systems created hundreds of digital twins so that users can pick the one they want to operate. Simulation makes it possible to evaluate service, inventory, shipment delays, transportation costs, expediting costs and more in future-state digital supply chain twins. 

Simulating at the order and shipment level enables individuals to calculate the true service rates of existing systems as well as proposed future systems. 

Service Is Crucial — Get It Right the First Time 

Service level is crucial — you have to get it right. Nobody cares about the 99% that got there on time. It’s the failing 1% that will crush you. Simulation maps out the 1% that didn’t make it, allowing you to correct failures before they happen. Avoiding failure is essential in hyper-competitive service environments. 

Inventory is an essential insurance method that’s used when running a network to buffer against variability and time delays. In a world with no variability, the inventory needed based on replenishment times might be 28 days. However, in the real world, replenishment shipments might take 51 days, meaning you sometimes need twice as much inventory. 

Through historical data, simulation gives a preview of the risks inherent in any network while providing a shape and form to the uncertainty that helps manage risk with the tools at our disposal: inventory, backup suppliers, expedited transport modes, etc. 

The “Unknown Unknown” Inventory Risks 

We know we can use simulation to mitigate “known risks.” But what about other unexpected sources of variability? The external risks we can’t model in simulation have put billion-dollar companies out of business. 

Former United States Secretary of Defense Donald Rumsfeld famously stated, “... there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns — the ones we don't know we don't know.” 

“Unknown unknowns” must also be considered in inventory strategies. Supply chain design technologies that identify and quantify risks at the supplier, facility, customer and network levels allow organizations to make better decisions where risk is considered in alternate scenarios alongside finance, service levels and sustainability. 

This external risk rating is a complement to inventory simulation. Used together, this technology enables companies to model and simulate hundreds of potential future-state supply chains while quantifying and reducing external risks. 

Don Hicks is president and chief executive officer of Optilogic