Prescriptive Analytics Techniques for Decision Optimization

Prescriptive analytics is the practice of choosing actions, not just estimating outcomes. It takes inputs like demand forecasts, costs, capacities, service targets, and policies, then computes a decision that optimizes an objective such as cost, profit, time, or risk. The key difference is that outputs are decisions (routes, schedules, assignments, allocations) that can be executed. 

The most common techniques are optimization-based: 

• Linear programming for continuous allocation and planning decisions 

• Mixed-integer linear programming (MILP) when on-off, yes-no, or assignment decisions matter 

• Network flow models for routing and distribution structure 

• Goal programming or multi-objective approaches when tradeoffs must be balanced 

Heuristics and simulation can complement these, but optimization is the workhorse when constraints and objectives must be enforced consistently. 

Predictive models estimate inputs to an optimization model such as demand, travel time, churn, or failure likelihood. Optimization uses those inputs to decide actions, such as inventory levels, shipment plans, or maintenance schedules. A typical pattern is predict, then optimize, then monitor outcomes and refresh inputs. Gurobi serves as the optimization solver that turns a formulated model into a solution.

Most business optimization models can be summarized as: 

• Decisions: what you control (how much to produce, which supplier to choose, who works which shift) 

• Objective: what you want to improve (minimize cost, maximize margin, hit service levels) 

• Constraints: what must hold (capacity, labor rules, budgets, contract limits, safety stock, time windows) 

Prescriptive analytics adds value when constraints are numerous and interacting, making manual planning fragile. 

Typical decisions in supply chain include multi-echelon inventory targets, production quantities, lane selection, and shipment consolidation. Constraints include plant capacities, lead times, minimum order quantities, and service level requirements. KPIs include total landed cost, fill rate, and on-time delivery. Optimization helps coordinate decisions that otherwise get made in silos, such as production planning that ignores transportation bottlenecks. 

Scheduling models assign people to shifts and tasks to meet demand while respecting labor rules (breaks, maximum hours, skills, seniority, fairness policies). Objectives often combine coverage quality and cost (overtime, temporary labor). MILP is common because assignments and shift selections are discrete decisions. If you stop early, the solver can return the best incumbent schedule found and an optimality gap, which is important for operational acceptance. 

Portfolio optimization, capital allocation, and asset-liability management are common. Decisions might be holdings or hedges, objectives might balance return and risk measures, and constraints cover exposure limits, liquidity, and regulatory requirements. These are typically deterministic optimization models, and uncertainty is handled by running scenarios, stress tests, or robust formulations rather than assuming the model predicts the future. 

Treat uncertain inputs as a set of plausible cases rather than a single truth. Common patterns include scenario planning (optimize per scenario, or choose decisions that perform acceptably across scenarios), parameter sweeps to understand sensitivity, and robust or stochastic formulations at a high level. No method guarantees protection against all futures, but these approaches can make decisions less brittle. 

Prescriptive analytics techniques turn data into decisions by optimizing objectives under constraints. Linear programming and MILP are central tools for decisions in supply chain, workforce planning, and finance, especially when tradeoffs and policies must be honored consistently. 

When you can state the decision, objective, and constraints clearly, an optimization solver like Gurobi can produce a proven optimal solution when solved to completion, or a best available solution with a reported optimality gap when time-limited, enabling decision optimization that is auditable and operationally relevant.