For most involved in Supply Chain Management, optimization is viewed as one of the three primary methods to create a supply or central plan that matches or balances assets with demand. Historically effective use of space involved minimizing unused space or maximizing revenue from a fixed amount of space. COVID-19 has upset the social order.
Optimizing your supply chain involves looking at the entire process, and not just the initial solution. Here’s an example of how.
Use this example as a starting point to understand the different optimization methods, and when optimization is helpful in supply or central planning.
I work with clients that utilize our supply chain optimization software to maximize their resources. In my upcoming webinar “Should I Optimize My Supply Chain Planning?” I’ll dive deeper into the concepts of supply chain optimization and show examples of when it’s ideal to optimize and when it’s less ideal. In today’s blog post, I’d like to simplify this concept by looking at some basic equations and scenarios to explain how “solvers” or supply chain optimization algorithms work.
If you are looking to improve your supply chain management systems in 2018, you most likely have asked the common question: How do I assess my current efficiency? This is a good starting point for anyone looking to add functionality or identify loopholes within current processes. There is no single perfect method that meets all needs and has no flaws. However, the good news is, supply chain assessments have proven to be very advantageous for many businesses.
The use of optimization in supply chain management is widespread, just not in supply planning. Regular use of optimization occurs in inventory management and demand forecasting. “Best-fit straight line” is one of the most common uses of optimization. With this method, you enter or pull into Excel (or your favorite statistics software) a set of “x values” (the independent value e.g. the number of cars in a train) and a set of “y values” (dependent value e.g. the fuel cost for each train), click a few buttons and you get a “best-fit” straight line – a slope (b1), a y-intercept (b0), a measure of goodness, and a straight line drawn through your scatter plot.
An optimization model does the same; it calculates the decisions based on the stated preferences and constraints in the model. That can sometimes have the inadvertent effect of finding new pathways, a road less, if ever, traveled.
In my role as the Director of Analytics, I enjoy working with the Arkieva team and our clients, in building optimization models which help organizations make intelligent decisions with regards to meeting demand, capacity allocation, inventory levels, factory schedules, forecasting, and cancer research. These models are built using a variety of mathematical methods including Boolean