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Data science and “big data” are hot topics right now, and for good reason.

With the exponentially increasing amount of IoT-connected devices, pulling data from these devices and sending them to manufacturers is becoming increasingly relevant. This data can be used for a variety of high-value purposes, including predictive maintenance, market price analysis, demand forecasting, and warranty analysis.

Predictive Maintenance Programs for Continuous Improvement

Perhaps the most prominent use of data science in manufacturing is for predictive and preventive maintenance. Data scientists use data from a variety of sensors and track that data against the optimal reading to determine if a piece of machinery is about to have a failure.

Contrast this with reactive maintenance, where maintenance activity takes place after the disruption has occurred, or scheduled maintenance, where a predetermined inspection and repair schedule is followed, regardless of how the machine is performing.

With predictive and preventive maintenance, modern machines, or legacy machines retrofitted with IoT capabilities, collect data through sensors mounted in various places along the production line. Those sensors can scan a piece of equipment and send the data they collect to the manufacturer in a central hub, either in the cloud or an on-premise server

After that, the data scientist can read and interpret that data through predictive modeling, and determine not only whether the machine is about to have a breakdown, but also what the next best course of action will be. 

For example, say a piece of equipment is determined to have a deficiency that will soon impact the manufacturing operation. You could proactively repair the part, sure — but is that the most efficient use of your resources? Are there adjustments you can make to the hardware that would be more cost-effective? Alternatively, maybe there are more maintenance costs associated with fixing the part than with replacing it altogether, depending on the structure of your operation. 

Predicting the Market Price of Products

Prices fluctuate on products constantly — whether it’s due to the time of year, the demand for the product, or limited availability that is driving the price. The market prices of your products will often be in flux. Being able to determine the optimal price for the product you’re manufacturing based on current and even future market prices will increase your margins dramatically. 

Using approaches like ensemble tree-based and deep learning methods for predictive modeling, as well as univariate and multivariate time series prediction and forecasting, data scientists can anticipate changes in market prices and use machine learning to make recommendations on the next best action for the business to take. 

Demand Forecasting with Machine Learning

Similar to price prediction, demand forecasting, manufacturing, and data science go hand in hand. Properly forecasting demand will determine your optimal supply rate, which, in turn, minimizes unnecessary manufacturing or inventory holding costs and streamlines your supply chain. 

If the demand for a product suddenly rises unexpectedly and the manufacturer cannot keep up with the demand, you’re likely to miss sales opportunities and see the impact on your bottom line. 

On the other side of the coin, sitting on giant inventory stocks that you can’t empty isn’t great for business, either. Forecasting demand is a key component in keeping your manufacturing on target.

In these scenarios, data scientists can build complex machine learning models and applications that analyze historical demand data from sales, marketing, and financial sources (e.g. CRM, ERP, POS systems, market studies, and more) to make reliable predictions about future product demand. 

In this area, as with all data science projects, the quality and cleanliness of the data will make all of the difference in the outcome. If your CRM or ERP is notoriously unreliable, or if you know that your POS systems are used inconsistently throughout the business, it is likely your predictions will be unreliable and inconsistent, too. Your first step in those cases will then be to clean up the data. 

Using Data Science for Warranty Analysis

Warranty claims are an area where manufacturers stand to lose a considerable amount of money, not only in payouts for defective products but in lawsuits and brand damage if a malfunctioning product causes injury or makes the news. 

Ideally, your products would ship and operate perfectly up until their warranty date and beyond. Living in the real world, however, we know that’s not a pragmatic approach. Product issues will occur, and claims will be made. Innovative companies are those that look at every claim made as a new opportunity to improve.

Manufacturers can create a system for analyzing the data with every warranty claim that comes in, to determine if their product is having issues in the same areas time and time again. Using data science, manufacturers can then determine the root causes of the product issues and iterate on the design of the product to lessen the number of warranty claims that come in, and in turn, increase their product and customer satisfaction.

Adjusting to a World Where Lean Manufacturing is the Norm

Data science is disrupting manufacturing in a big way right now. Even older cemented manufacturing companies are having to adopt the practice to keep up. Lean manufacturing is the “norm” now, which is causing companies to adopt continuous improvement programs at every angle and attempt to eliminate waste. 

Data science done well can result in increased productivity and profit, as illustrated by the examples above. To be successful, however, you’ll need to make sure that your data science team asks the right questions, measures the right things, and defines optimal metrics in the ways that are most meaningful for your business.

Going further, the insights and models need to be integrated into existing systems or even a standalone application, so that end users can use the predictions and recommendations to enable more effective decisions. At Very, we find it particularly effective when this is done with data scientists working as integrated members of a full-stack Industrial IoT development team. 

With the assistance of data science and the many benefits it can bring, manufacturing companies can create, price, and distribute products as intelligently as possible.

Looking for an IIoT development firm with a strong data science practice? That’s us. Tell us more about your machine learning project today.