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.