Optimization & Simulation
While predictive analytics tell you what will happen, prescriptive analytics tell you what to do about it. Optimization and simulation go beyond forecasting to find the best decisions given constraints, trade-offs, and competing objectives.
Optimization & Simulation Overview
Optimization and simulation are most valuable when decisions are constrained, trade-offs are real, and intuition breaks down. In production planning, capacity allocation, logistics, pricing, and product mix, the best decision is rarely obvious and is often counterintuitive once constraints are applied.
Sea Cliff brings deep, hands-on experience applying linear and mixed-integer optimization to real operating environments. Our work spans production and capacity planning, batch and network logistics, pricing and mix optimization, and other constraint-heavy decision problems where feasibility, performance, and explainability all matter.
We focus on building optimization and simulation models that reflect how the business actually operates. This includes realistic constraints, objective functions aligned to outcomes, and solution approaches that can be trusted, interrogated, and extended by client teams over time.
Formulate and solve linear and mixed-integer models for production planning, capacity allocation, logistics, pricing, and product mix. Emphasis is placed on model structure, constraint design, and solution quality, not just mathematical elegance.
Select and tune solvers based on problem structure and scale. Sea Cliff has extensive experience with Gurobi for complex and large-scale models, as well as open-source solvers such as GLPK and others when appropriate. We help clients understand when off-the-shelf approaches break down and how to address performance, stability, and feasibility challenges.
Build scenario models and digital twins to evaluate decisions under uncertainty. Test production disruption scenarios, capacity changes, demand shifts, and pricing decisions, including rebate and incentive analysis informed by price elasticity. These models allow teams to understand trade-offs and impacts before committing resources in the real world.
When Optimization Is Harder Than It Looks
Optimization problems often fail not because the math is wrong, but because the problem has been framed incorrectly. What organizations perceive as fixed constraints are often assumptions, legacy rules, or gut instincts that no longer reflect how the business actually operates. When these assumptions are embedded directly into models, they not only limit outcomes, but in some cases make the problem technically unsolvable because the underlying constraints are inaccurate.
Sea Cliff consultants apply a structured process decomposition approach to break complex decision problems into smaller, solvable components. By mapping how work actually flows and where decisions are made, we help organizations identify true constraints, eliminate false ones, and define optimization problems that reflect operational reality.
From Models to Decision Support
Sea Cliff designs optimization and simulation models as decision support systems, not one-time analyses. We work closely with client teams to validate assumptions, interpret results, and embed models into planning and operating processes.
Throughout delivery, we focus on enabling client teams to own and evolve these models themselves. By transferring knowledge, establishing clear model logic, and aligning outputs to real decisions, we help organizations build lasting optimization capability rather than ongoing dependency.
