Optimization is classically used to make the highest profit choices in supply chain applications at all levels.

In optimization applications, Materials are usually governed by multiple overlapping sets of rules and costs.  These rules represent both challenges and opportunities.  The challenge is to find the best solutions that follow all of the rules.  The Opportunities are to try different combinations in different circumstances that follow the rules and maximize the formal “objective function” – typically: highest service at lowest cost.

At Princeton Consultants, we group these rules/costs into: physical, regulatory, contractual, marketplace, policy, and practical.

• Physical: For the regional agribusiness, the crops have a high rate of perishability, so the optimization and reduction of the time from harvest to milling had a major impact on food quality and the price the company was able to obtain. For the biotech manufacturer, the product is urgent, personalized cell therapy that is highly perishable. Simulation identifies resource bottlenecks and best use of capital improvements; the scheduling optimization solution in part balances resource utilization.
• Regulatory: For the freight railroad, different classes of shipments had different legal characteristics.  For instance, Hazardous Materials (Hazmat)  have higher costs and limit the vendors and locations they can ship through.
• Contractual: for the business publication, different customers buy guaranteed advertising plans.  The company’s ability to promise and deliver on highly flexible and tailored plans with optimization has been key to attracting and retaining top accounts at margins higher than its competitors.
• Marketplace, Policy, and Practical: The printing company found that with optimization it could improve on the postal rates its magazine customers paid, putting money in its pockets and standing out in an otherwise highly commoditized industry. At the e-commerce trailblazer, the optimization model places components/products into containers/boxes and reduces the required number of box configurations to meet subscriber needs; the transformed performance allows the team to tweak inputs and re-run the model—and therefore evaluate different parameters, levels of subscriber aggregation, definitions of a “good” box, and even optimize on box value.