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Over the course of the early 2020s, businesses have faced a barrage of challenges that few could’ve predicted. From global supply chain disruptions and pandemic-driven shutdowns to on-and-off tariffs, these unexpected events have tested the limits of traditional planning strategies.

The recent United States government shutdown provided yet another reminder that well-established systems can come grinding to a halt, lasting a record-breaking 43 days and gridlocking everything from local project funding to air traffic control and food assistance programs.

Senior Data Science Strategist at Gurobi Optimization.

These scenarios demonstrate that rare and complex disasters are difficult to anticipate, and even harder to model and prepare for. While worst-case scenario planning has long relied on historical data and contingency buffers, what happens when history offers no precedent?

This is where mathematical optimization plays a key role in simplifying and streamlining planning efforts.

The worst-case scenario

If you’re familiar with optimization techniques, you may identify the process of optimizing under the worst-case as “robust optimization.” While it’s a noteworthy application of optimization, that’s not precisely what this article is about.

Instead, imagine that you run a mid-sized manufacturing company based in the United States. Your business relies on timely regulatory approvals in order to ship products across state lines.

During a prolonged government shutdown, these approvals stall, leaving your team with a steadily growing number of finished products and nowhere to send them. Inventory piles up, storage space disappears, employees idle with no work to do, and contractual delivery deadlines loom.

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This stalled scenario is complex and full of interdependencies. Storage limits, budget caps, and fixed workforce hours can’t be adjusted, and missed deadlines could trigger financial penalties and damage customer relationships.

There are certainly factors that you can control—production scheduling, resource and labor allocation, and alternative sourcing, for example—but none can be adjusted without having their own downstream impacts on your bottom line.

How might you effectively plan for this kind of situation?

The traditional response

Businesses have traditionally approached worst-case scenario preparation with manual planning methods. In our shutdown example, this might include your manufacturing company’s leadership team getting together to review spreadsheets, examine historical data, and brainstorm scenarios to estimate their potential impact.

In itself, this is an entirely plausible method. Your team could review a range of past data, talk through what the scenarios may entail in the future, and implement relevant contingency buffers as a means of proactive response. The success of this approach, however, is largely dependent on predictability.

It’s not unlikely that historical production data could help a manufacturer prepare for spikes in demand or the loss of a strategic supplier. But if there’s anything we’ve learned from the first half of this decade, it’s to expect the unexpected.

Volatile supply chains, fluctuating international markets, global conflicts, and widespread health crises are not as easy to predict and effectively prepare for.

And when they’re layered atop dozens of preexisting variables—storage limits, labor hours, contracts, and more—the traditional response quickly becomes slow, reactive, and generally inadequate.

Responding with optimization

Mathematical optimization offers today’s businesses a fundamentally different approach to worst-case scenario prepping. Rather than relying on gut instinct and static data, optimization leverages advanced algorithms to assess multifaceted challenges and provide the best possible solution.

Each mathematical optimization problem includes:

• An objective function, or the goal you’re trying to achieve.
• Decision variables, or the factors that can be changed.
• Constraints, or the factors that cannot be changed.

In our manufacturing scenario, these factors would be:

• An objective function of minimizing total cost while still meeting delivery obligations.
• Decision variables such as production levels, labor allocation, and outsourcing decisions.
• Constraints that include storage capacity, budget, workforce availability, and regulatory restrictions.

Your team could use these variables to create a mathematical model that represents your problem. Then, using an optimization solver to run the relevant algorithms, you could determine the optimal balance between competing priorities in minutes, rather than days.

The model might recommend reducing production by a certain amount, reallocating workforce resources to preventative maintenance projects, and/or outsourcing a portion of production to avoid storage overflow.

Whatever the recommendation, you can rest assured knowing it was informed by all relevant variables and determined to be the best possible solution.

Using optimization drastically streamlines the preparation process, making worst-case scenario considerations and decisions faster and more reliable.

Human decision-makers are still empowered to share key input and have the final say on how the team moves forward, all while outsourcing the time-consuming computational work to the power of steady, trustworthy, and easily updatable mathematical models and the advanced algorithms that solve them.

Towards a less frantic future

Worst-case scenarios are no longer as rare as they used to be. From pandemics and tariffs to lengthy government shutdowns, disruptions are more frequent, more complex, and more difficult to predict.

Mathematical optimization gives today’s businesses a smarter way forward. By taking on the legwork of scenario modeling and review, optimization can help transform lengthy and uncertain manual planning into proactive business strategies.

While an optimization model cannot completely eliminate unpredictability and risk—as no solution truly can—it can act as a metaphorical “rock in the storm,” helping teams react to their changing environment with fast and reliable problem-solving capabilities.

When the next unprecedented disaster strikes, optimization-driven businesses can rest assured in the results of their proactive efforts, rather than scrambling to make up for a lack of preparedness.

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Jerry Yurchisin is Senior Data Science Strategist at Gurobi Optimization.

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