Prescriptive Analytics Examples with Optimization and Gurobi

Prescriptive analytics examples: why they matter 

Forecasts and dashboards can tell you what might happen, but they do not necessarily tell you what to do. Prescriptive analytics examples show how to go one step further by recommending specific actions that respect real constraints and business goals. When you combine prescriptive analytics in for form of mathematical optimization using Gurobi, you get a decision engine that turns data and complexity into executable plans. 

Prescriptive analytics uses models that can take inputs such as demand forecasts, costs, capacities, and then determine the best decision according to an objective like minimizing cost or maximizing profit. Under the hood, these models often rely on mathematical optimization methods such as linear and mixed integer programming. Gurobi serves as the solver that processes the model and returns either a proven optimal solution, a proof that no feasible solution exists, or the best solution found within your selected time limits. 

From descriptive reports to prescriptive decisions 

Most analytics journeys move from descriptive to diagnostic to predictive, and then to prescriptive. Descriptive analytics describe what happened and predictive analytics tells you what is likely to happen. Prescriptive analytics answers the question: given what we know and what we expect, what should we do right now? 

To get there, you need to structure your decision problem. That means defining decision variables, constraints, and objectives in a way that reflects your operations. A prescriptive model for production planning might decide how many units to make of each product, subject to capacity and material constraints, with an objective of minimizing total cost. Gurobi uses a model that represents this to compute the recommended plan that maximizes or minimizes your objective.  

The key difference from rule based systems or spreadsheets is that prescriptive analytics with optimization considers the entire decision space of your model at once. It can see interactions that are hard to capture with isolated rules, especially when you have many products, locations, and time periods. 

Prescriptive analytics examples in supply chain and logistics 

Supply chain planning offers some of the clearest prescriptive analytics examples. Consider tactical production and distribution planning. Inputs include demand forecasts, production costs, transportation costs, ca

pacities, and lead times. The prescriptive model decides production quantities at each plant and shipment flows through the network to meet demand. 

Constraints enforce plant capacities, material balances, transportation limits, and minimum service levels. The objective function typically minimizes the total costs of production, inventory, and transportation costs while potentially penalizing for unmet demand. Solving this model with Gurobi gives a recommended plan that explains exactly how much to produce and ship, where, and when. 

Another prescriptive analytics example is transportation load planning. Here, decisions involve assigning shipments to lanes, choosing between modes, and building loads. Constraints can be used to represent trailer capacities, time windows, driver regulations, and service commitments. With Gurobi, you can generate daily or weekly load plans that balance freight cost and service quality, then run what if scenarios to test new lanes, capacity limits, or contract structures. 

Prescriptive analytics examples in workforce and services 

Workforce planning is rich with prescriptive analytics examples. For a call center, the model decides how many agents to schedule for each shift and skill group. Inputs include forecasted call volumes, service level targets, wage rates, and labor rules. 

Constraints can enforce minimum coverage by time period, maximum hours per agent, and skill qualifications. The objective might minimize labor cost while achieving target service levels across queues. Gurobi solves this mixed integer programming model and returns a staffing plan aligned with both cost and service goals.  

In field services, a prescriptive analytics model might assign technicians to jobs over a planning horizon. Decisions include which technician visits which customer and at what time. Constraints capture travel times, skill requirements, time windows, and working hour limits. The objective could minimize total travel time or maximize jobs completed. Gurobi computes technician routes and assignments that respect these constraints and can be fed into dispatch systems. 

Prescriptive analytics examples in pricing and finance 

Pricing and revenue management also provide useful prescriptive analytics examples. For instance, a retailer might decide price levels by product and period within specified ranges. Constraints enforce price relationships across tiers or brands and limit the frequency or size of price changes. 

The objective might maximize expected margin using demand curves estimated from predictive models. Here, Gurobi does not forecast demand; it uses the forecast to choose the price combination that best meets your objective within the modeled constraints. 

In finance, capital allocation problems can be modeled prescriptively. Decisions determine how much budget to allocate across projects or assets. Constraints represent budget limits, regulatory requirements, and risk measures that can be handled linearly or via convex approximations. The objective maximizes expected return or another performance measure. Gurobi processes the optimization model and returns the recommended portfolio allocation, along with information about solution quality. 

Conclusion: from examples to enterprise practice

These examples highlight how using optimization can move organizations from insight to action. By modeling decisions, constraints, and objectives explicitly using mathematical optimization and solving with Gurobi, you can generate repeatable, transparent recommendations for supply chain planning, workforce scheduling, pricing, and capital allocation. 

A practical next step is to identify one decision area where trade offs are clear and data is reasonably available. As that pilot delivers value, you can expand scope, refine formulations, and integrate prescriptive analytics more deeply into your planning processes. Over time, optimization powered prescriptive analytics becomes a core capability for making better, faster, and more consistent decisions.