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Predictive Analytics in Manufacturing

In the manufacturing and distribution industry, which relies as heavily on data as any industry does, predictive analytics—a branch of machine learning—is the coin of the realm.

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In the manufacturing and distribution industry, which relies as heavily on data as any industry does, predictive analytics—a branch of machine learning—is the coin of the realm.

Data these days is what makes businesses go, now more than at any time in history. In the manufacturing and distribution industry, which relies as heavily on data as any industry does, predictive analytics—a branch of machine learning—is the coin of the realm. It allows manufacturers to find hidden patterns, find trends that could lead to improvements, continue meeting customer demands, market the business to the right audiences and keep a leg up on competitors.

The single most useful and critical source of information regarding how the manufacturer is doing in managing data and running the business is, of course, the customer. There are other critical elements—inventory, internal controls, workforce readiness and more—but none are as important as how the customer is responding. So with this level of reliability on customer feedback, predictive analytics provides an invaluable tool to notice and respond to industry trends and to ensure customer satisfaction is being maintained.

Predictive analytics helps determine the quantity needed of certain existing products—in addition to new products and inventory—through the use of algorithms and statistical models. This is part of artificial intelligence (AI) and relies on patterns and inference; to put it as simply as possible, it builds a mathematical model from sample data, and then offers predictions or decisions without being programmed to perform a specific task.

Manufacturing companies see a natural ebb and flow of activity throughout the year; times of high inventory and production, times of intense customer demand, as well as times when things are slower. What machine learning can do in these instances is help predict demand based on customer needs at particular times, and then use that information to share with suppliers, distributors and others. It becomes an invaluable tool in ensuring everything remains operational and efficient.

Predictive analytics also plays an important part in areas such as transportation and logistics. Manufacturers are constantly beholden to precise deadlines with customers, and machine learning can create a higher level of certainty in knowing those deadlines will be met and customer needs will be satisfied. Think of how critical it is for manufacturers to know the number of stops needed for deliveries, or traffic patterns and possible delays. This is the value that predictive analytics can bring.

By applying machine learning tools to their data, those companies are able to optimize resources, meet objectives and identify customer trends and needs. When combined with state of the art technology employed for modern manufacturing, predictive analytics can be a major game-changer for manufacturing companies looking to remain a cut above the competition.

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