This article is part of an ongoing series on the uses for artificial intelligence (AI) in manufacturing, starting with our article introducing machine learning and AI, and their relevance to manufacturing.
This article will cover how AI can be used for capacity planning, the process of determining how much output a manufacturer must be able to create under certain demands. Demand forecasting is one thing, but resource allocation in response to certain level of demands is what capacity planning is all about.
Implementing AI can be a major key in generating and retaining a high level of efficiency in a manufacturing process, including the prediction of production capacity.
Use It or Lose It
In our article on demand forecasting, we told you about how AI can be used to predict the demand for certain products, helping you hit that Goldilocks zone where you are neither overstocking or under-stocking stores with your products. With demand forecasting, the goal is to produce just enough items so that you are consistently maximizing products and customer interest without spending too much on resources.
Demand forecasting can tell manufacturers a truth they likely already know, which is that demand, even when accurately predicted by an AI agent, is a constantly shifting thing, and these changes must be accounted for if a business wants to stay afloat, let alone maximize profits.
As a result, manufacturers must be prepared for all kinds of changes in demand. The process for figuring out how to manage resources based on demand is called capacity planning.
Capacity planning is essential for manufacturers who want to ensure that no resources go to waste, or, alternatively, that they are not unprepared for a particularly large order. It should come as no surprise that artificial intelligence, which is inhumanly efficient at prediction tasks, can do wonders for upping your capacity planning game.
Using AI Makes for Quicker, More Accurate Capacity Planning
At any point of time, a manufacturer has a capacity for production, meaning how much of any one item that it can produce.
A lot of data is needed for capacity planning. You will need to know how much of each resource you have, as well as which workers will be present during the course of a production timeframe, how well the machines involved are working, and more.
Luckily, AI is made for big data analysis, so there is no trouble there.
The main goal of capacity planning is to allocate your resources correctly, from staff scheduling to commodity orders, based on how many items will need to be produced given a certain level of demand.
So, finding the demand is one part of the process. As mentioned, AI offered by Findability Sciences can already handle demand forecasting and produce highly accurate results, so you are already covered there.
Capacity planning is great for long-term planning as well. Since it is at heart a process about prediction, you can set hypotheticals to a capacity planning AI agent to find out your needed resources given certain levels of demand. This way, you will not need to experiment or learn on the fly, but plan ahead, and even for the worst.
Any manufacturer with their salt knows that there are fallow periods and there are insanely productive periods, and that what matters most is how one prepares for either period. Order too many supplies in a fallow period, and you will lose out on money. Order too little in a productive period, and you will miss out on maximizing profit.
Oftentimes, and this goes for any industry, anticipation and preparation are as vital as action and reaction, and this is what capacity planning is for.
Lastly, you should absolutely get creative with using AI processes for maximizing efficiency. As mentioned, demand forecasting and capacity planning go hand-in-hand when it comes to resource management, but you should also consider other services offered by Findability Sciences, such as commodity price prediction. However you use it, AI is sure to make you a better manufacturer.
Previous Articles in Our Machine Learning for Manufacturers Series: