Optimization is Key, But Which Project Unlocks the Most Value?

In an increasingly competitive and dynamic business environment, optimization has become a cornerstone for success across various industries. From manufacturing and supply chain to retail and aviation, companies are leveraging advanced optimization techniques to streamline operations, reduce costs, and enhance customer satisfaction. Global giants like Amazon have used optimization algorithms to revolutionize their logistics and warehousing strategies, while airlines employ optimization models to schedule flights and crews, maximizing efficiency and profitability.

However, the path to optimization is not without its challenges. In the business world, uncertainty is an ever-present factor. Some problems are fraught with unpredictable elements—fluctuating demand, variable lead times, or changing market conditions—while others are more stable and predictable. The presence and degree of uncertainty in a problem can greatly influence the outcome of an optimization project. This variability, coupled with the fact that organizations typically operate within the constraints of a fixed budget for the financial year, makes it crucial to carefully choose which projects to pursue.

To navigate this complexity, organizations can benefit from a structured approach to decision-making. One such approach is the Optimization Priority Quadrant (OPQ), a framework that categorises potential projects based on the level of uncertainty involved and the value they are likely to generate. By using this framework, companies can prioritise their optimization efforts, focusing first on projects that promise high returns with minimal risk. To illustrate this approach, let’s consider how it applies within the Consumer Packaged Goods (CPG) industry.

Optimization Priority Quadrant (OPQ) Framework

1) High Value, Low Uncertainty

In this quadrant, projects are characterised by well-understood parameters and high potential returns.

A prime example is Distribution Planning with Truck Load Optimization. In this solution, a company optimizes the distribution of its products from factories to distribution centers (DCs) and from DCs to distributors, minimizing operational costs while adhering to lead time and safety stock constraints. All the data, such as costs, storage and transportation capacities, and distances between facilities, are known with little or no uncertainty. Investment in such a project can yield substantial cost savings with minimal risk.

Another example is Last Mile Vehicle Routing, where a company empowers its distributors by optimizing vehicle routes for deliveries to retail stores based on purchase orders. With known factors like product demand, LTL/FTL costs, truck capacity, and customer locations, this solution can significantly reduce logistics costs, especially in last-mile delivery. Both projects offer significant cost savings with minimal risk, making them top priorities for CPG companies.

2) High Value, High Uncertainty

Projects in this quadrant involve substantial uncertainty but offer the potential for high rewards. Pricing Optimization is a key example, where companies strive to set optimal prices for their products to maximize revenue and profitability. However, pricing decisions are influenced by numerous unpredictable factors, such as market demand, competitor actions, and consumer behaviour, making it a complex and uncertain process. Another example is Trade Promotion Optimization, which aims to maximize the effectiveness of promotional activities. While promotions can drive significant sales, their impact is often uncertain due to varying consumer responses and market conditions. Despite the high uncertainty, the potential value of successfully optimizing these areas is immense, justifying their pursuit after more certain, high-value projects.

3) Low Value, Low Uncertainty

This quadrant typically includes projects that, while straightforward, offer relatively low returns. Workforce Scheduling for manufacturing plants and warehouses is a typical example, where companies optimize the allocation of workforce to meet production and warehousing demands. The parameters, such as available workforce, shift patterns, and production schedules, are usually well-known and stable, leading to low uncertainty. However, the financial impact of optimizing workforce schedules is generally modest compared to the above projects.

4) Low Value, High Uncertainty

Projects in this quadrant are characterised by high uncertainty and relatively low potential value, making them less attractive as immediate priorities. Inventory Optimization for Raw Materials serves as a fitting example for a CPG company. Optimizing raw material inventory can reduce holding costs and avoid stockouts. However, in an environment of frequent new product launches, the uncertainty in demand for these materials and the variability in supply chain conditions can make the optimization process challenging. Moreover, the overall value generated by such optimization is often limited, especially when compared to finished goods or more critical resources. As a result, these projects are typically deferred until more impactful opportunities have been addressed.

It's important to note that "low value" in this context is RELATIVE. Even in the low-value quadrants, optimization projects can still deliver significant benefits compared to non-optimization initiatives.

The Optimization Priority Quadrant is a powerful framework for prioritizing optimization projects in any industry. It helps companies focus on projects that offer the highest impact with the least risk. By using this framework, organizations can ensure that their resources are directed toward the most effective and impactful projects.