The Value of Optionality in Supply Chain Planning

A modern supply chain lives with constant uncertainty. What if demand spikes? What if a supplier misses a delivery? What if a port slows to a crawl? Or a plant goes down for maintenance?

To cope with this uncertainty, “what-if” simulations have become an indispensable practice in modern supply chain management. This kind of scenario analysis gives leaders the ability to anticipate the impact of disruptions before they happen and enables accelerated decision-making when things don’t go according to plan.  

By stress-testing a supply chain network and comparing scenarios, you can identify vulnerabilities without risking real-world consequences. This proactive visibility empowers teams to evaluate trade-offs, compare mitigation strategies and quantify the operational and financial impact of each option.  

Ultimately, what-if simulations transform supply chains from reactive systems into more resilient, strategically planned ecosystems that can absorb shocks and adapt quickly in a volatile world.

And, most importantly, a scarce commodity in supply chain leadership is gained: time to think before you must act. ‍

Speed Versus Perfection

Despite its status moving beyond a “nice-to-have,” what-if simulations using optimization algorithms can be surprisingly challenging because these models often require lengthy solves and can be sensitive to even small changes in assumptions.  

Each scenario—whether adjusting demand forecasts, altering capacity constraints, or simulating supplier disruptions—requires the optimization engine to completely re‑solve a complex mathematical model, which can lead to long run times and significant processing costs. Additionally, many optimization models are finely tuned for a single “best‑fit” solution, making it difficult to explore wide scenario ranges without redesigning constraints, relaxing assumptions, or re‑balancing objectives.  

The result: Teams often struggle to iterate quickly, limiting their ability to test diverse scenarios at scale. These challenges make it essential for organizations to adopt more flexible what-if analysis. While there is value in the one true solution, there is value to getting an answer that is “close enough” to the optimal solution, while providing valuable “what-if” flexibility.

Re-Examining the Purpose of the Optimization Algorithm

Today’s commercial optimization algorithms are often positioned as a Swiss-army knife—a go-to, all-in-one solution for supply chain decision-making that can solve anything, anywhere. While optimization algorithms are unique in their ability to solve complex problems such as solving for the lowest cost sourcing/production network in complex environments, the time to solve such problems is lengthy. While scenarios like these are valuable to make strategic decisions, these decisions are not re-visited on a daily or weekly basis. The tactical planning trade-offs that need to be solved on a daily or weekly basis are typically much more restricted in terms of options.  

For these narrower, well-structured decisions, organizations don’t always need the heavy computational machinery of a full optimization engine. Rule-based heuristics, decision trees, linear scoring models, and even advanced analytics scripts can deliver a reasonable answer with far less complexity, faster run times and greater transparency.  

In these cases, it’s worth asking an almost heretical question: Do we really need the full computational machinery of an optimization solve for this decision?

The faster run times that result from using an alternative to optimization algorithms can allow for flexibility in planning and the ability to use what-if simulation tools. And once you make that shift, something interesting happens: planning becomes more flexible, because it becomes more iterative. You can test. You can learn. You can run one more scenario because it doesn’t cost you an entire afternoon. This faster speed doesn’t just improve planning. It will change the culture of planning.

The Value of an AI-Based Solver

In the past, the knock on rules-based heuristics was that they can head down a bad path and get locked into a solution that is far from optimal. With the increased speed of cloud computing, AI optimization alternatives like ketteQ’s PolymatiQ™ Solver offer a modern way to dynamically orchestrate multiple heuristic approaches and still reach high-quality solutions for common supply chain problems.

Instead of relying on a single, highly-structured optimization engine, these hybrid approaches deliver results that are highly accurate, significantly faster and far more scalable for everyday planning challenges, allowing organizations to gain near-optimal insights without the heavy computational overhead.

“When the cost of asking ‘what if’ is low, the impact is measurable. We’ve seen customers unlock 13% more capacity without new capital investment and drive over millions in projected annual savings simply by running faster, more iterative scenarios that align production, inventory, and demand in real time.” – Sneha Bishnoi, ketteQ VP Product Management

When the cost of asking “what if” is low, decision confidence skyrockets.