Deciding how best to use limited resources is a universal issue that spares no individual or business. In today’s competitive quarter-to quarter environment, it’s critically important to your company’s profitability (and longevity) that its people and assets are used most efficiently. While this is an obvious truism, it is not always obvious how to go about doing this—especially when dealing with complicated networks.

The companies going from “good” to “great” arrive at the right answers when it comes to making business decisions on product mix, logistical routing and demand planning through a process commonly known as linear programming (tabbed “linear” because the maximized or minimized answers are assumed to be linear in nature). This mathematical exercise squeezes profitability to the closest dollar while considering constraints of the business—limitations such as radius of delivery, fleet size, manufacturing capacity, amount of material, lead times, space, etc. When done correctly, the business can rest easy knowing there is not significant value being left on the table. However, many companies shy away from the technology because they just don’t understand it.

The purpose of this article is to demystify the complexity behind optimization by illustrating how it works in a case study context.

The Situation

The North American division of a global manufacturer was becoming progressively more capacity-constrained due to bottlenecks in its distribution network, while product demand in key regional markets was growing in disproportion to the regional plant’s ability to supply. The CEO made it a key priority to address this in order to increase profitability. However, due to the complexity of the distribution network and trade-offs between economic drivers, it was not a simple “back-of-the-envelope” exercise to quantify the opportunity and other costs of these bottlenecks. Assessing the overall impact of potential options was also very difficult.

In response, a project team composed of internal and external supply chain professionals used a framework to help the company’s executives refine the strategies for upcoming growth without hurting current customer obligations. The project’s objectives were threefold:

  1. Identify and value strategic network opportunities.
  2. Maximize operational profits.
  3. Provide visibility on future month-by month system constraints (e.g., material shortages, railcar availability, silo capacity).

The results of the project were phenomenal. The team confidently recommended:

  • A rebalancing of product to 25 markets (without sacrificing sales) in order to ensure greatest overall profit margin.
  • Tactical distribution moves to save over 5% in freight and other variable costs.
  • Payback analysis on the exact benefits of resolving the top network constraints.
  • A forecasting tool to measure profitability and network impact of future greenfield locations.

Network optimization is commonplace in most logistics-based industries and is not inherently difficult to execute, provided some basic understandings of the fundamentals are in place. With any optimization exercise, there are three parts: inputs, the process and the output (Figure 1). In this case the company wanted the output to reflect maximized profits (not necessarily just minimized costs.)

The Constraints (aka inputs)

First, the rules of the game must be established in the form of operating constraints, which form the “dimensions of the field” that the solver will ultimately play in. Demand is viewed as a constraint to a linear program (LP) since a location can only be given so much product before it exceeds its ability to convert the product into sales. In the client’s case, the company had 25 sales locations to consider, each with varying demand for the same product.

Another sideline to the field included margin information so that output would be based on overall profit. Specifically, this information detailed the individual product, fixed and delivery costs.

Finally, the capabilities of the network were entered, from plant manufacturing rates to railcar and truck capabilities.

With these rules in place, the team went to work ensuring they could replicate the previous year’s operational performance and thus establish a trustworthy baseline.