Incorporating Data Science Into Manufacturing Improves Quality and Efficiency

Business Finance

Incorporating Data Science Into Manufacturing Improves Quality and Efficiency

Manufacturing is one of the most integral and competitive industries in the United States. The race to become the most efficient is tight, and companies are applying insights from data science to their processes to improve their operations further every day.

The way artificial intelligence is used in smart factories across the country looks like something from an episode of the Jetsons.
When you see how big data is changing the world, it makes you wonder if data scientists may have the sexiest job of the 21st Century. They’re creating a world so full of wonder it’s fit for a kid. Continue reading to learn some of the incredible ways data science and machine learning are being used to improve manufacturing quality and efficiency.

Preventive Maintenance

When a manufacturing plant’s machines malfunction and they have to fix or replace them, it often causes production to go into downtime or even a complete shutdown. Even during a shutdown or short gap in production, there’s a chance you’ll have to pay unemployment benefits. So, not only are you not making money, but you also have to keep spending it. That’s a losing recipe in business.

One of the most promising features of big data and data science is the use of predictive analytics to forecast future events with precision and high speed. For example, data science issued by law enforcement agencies to predict where a criminal may be headed. If cops can trust it to predict where a criminal may strike next, you can certainly benefit from using data visualization and predictive analytics to see maintenance needs before they arise.

With the ability to see maintenance problems before they arise, you can schedule repairs in conjunction with normal lulls in production to prevent downtime-losses. For example, many manufacturers use a CNC plasma cutter and a plasma table for the high speed of plasma cutting. If you’re one of many fabricators who relies on plasma to get the job done, you need your CNC plasma cutter to be in good shape. If you know your CNC plasma cutting machine is nearing the end of its lifespan weeks or months before it happens, you can schedule Messer Cutting to install a new one for you in advance without missing a beat.

Demand and Production Forecasting

The manufacturing industry is driven by demand more than any other. By the time a retailer notices that an item has fallen out of demand, it’s already on their shelves, and the same is true of distribution and fulfillment centers too. Manufacturers are the only ones in the supply chain whose operations are dictated by demand.

Data scientists have found a way to mitigate some of the effects of demand on the production/supply end of the supply and demand equation. When you can forecast changes in your products’ demand, you can modify your operations to surf the waves of volatility.

Pricing Analysis

When you develop a new product, it can be hard to determine its market value. You don’t want to price it too high and limit your potential customers. On the other hand, you don’t want to price your product too low and cheat yourself out of profits.

You can apply big data to use key factors such as the time it takes to make the product, the cost of materials that go into each model, and the number of labor hours used as well. Using those factors, a skilled data scientist can extrapolate appropriate prices for your products. As you can see, big data isn’t just about predicting malfunctions and demand changes‚Äîit’s an excellent tool for solving complex problems like pricing and supply chain management as well.

Energy Efficiency

It should come as no surprise that energy is one of the most significant manufacturing costs. Believe it or not, you can even use big data to optimize your energy use.

Energy efficiency is all about getting your machines to do the same work while using less energy. While we may never be able to eliminate energy waste, with big data, you can find areas of your operations that are the most energy-draining and develop targeted solutions to conserve energy.

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