Deciding how to use resources is a universal business concern.
In today’s hypercompetitive ecosystem, it’s vital to your company’s fiscal health that its people and resources are used most effectively. When dealing with complicated networks, achieving maximum efficiency can be challenging.
Companies looking to maximize resources often make decisions on product mix, logistics and demand planning through a method commonly referred to as linear programming. This numerical exercise pushes profitability to the nearest dollar while taking into consideration constraints of the business, such as distance for delivery, shipping capacity, manufacturing capacity, quantity of raw material needed, lead times, warehouse space and other factors.
When done right, a business can relax knowing there is not considerable value being left up for grabs. However, a lot of companies stay away from numerical methods because they find them difficult to understand.
Determining the Constraints
Network optimization is prevalent in most logistics-based industries and is not fundamentally challenging to execute, given some basic understandings of the established principles. have been established. With the typical optimization routine, there are three parts: Inputs, process and output.
For a network optimization, the inputs are the constraints of the supply chain. Constraints might be number of clients, costs (including fixed and delivery costs), manufacturing rates and shipping capacities.
The Optimization Process
Also known as linear optimization, the process is intended to either maximize something positive, like profits, or minimize something negative, like costs. The optimization might consider multiple constraint factors, like inventory space, raw materials and labor hours, to make decisions.
While the process considers all the limiting factors, it also looks at the variables behind them within the given thresholds. The objective is to locate where the most limiting constraints can be met while maximizing profit. Computer-based algorithms can sort through thousands of variables until it has discovered the best solution.
The Output and Bottlenecks
Despite diligently outlining constraints and utilizing a proven, robust process, the results of an optimization procedure can still generate a non-feasible result. For instance, an optimization can suggest that a company minimize shipping costs by not shipping product at all.
An output analysis can also reveal bottleneck in the supply chain. Market demand, capital, equipment capacity, time and other factors can prevent continuous flow of product. While some bottlenecks are captive to an essential supply chain factor, others are unnecessarily costing the business in sales or excessive labor.
One way to spot bottlenecks is to look at how increasing throughput affects overall profit return. This may allow for the isolation of a single issue that when “fixed,” results in an easier flow of product. Without the ability to model a supply chain, it would be difficult to see critical bottlenecks and their accompanying impacts on profit.