3. Data Science Is Key To Forecast Demand
Properly forecasting demand will determine your optimal supply rate, minimize unnecessary manufacturing or inventory holding costs, and streamline your supply chain.
If the demand for a product suddenly rises and you can’t keep up with the demand, the missed sales opportunities impact your bottom line. On the other side of the coin, sitting on giant inventory stocks that you can’t turn at an optimal rate isn’t great for business, either. Forecasting demand is a key component in keeping your manufacturing on target.
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 unreliable, or if 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 be to clean up the data.
After cleaning the data, 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.
4. Data Science Powers Analysis of Warranty Claims
Manufacturers stand to lose a considerable amount of money on warranty claims, 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. Practically, however, we know that’s not a realistic outcome. Product issues will occur, and claims will be made. Innovative companies look at every claim made as an opportunity to improve.
Manufacturers can use data science to 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. Manufacturers can then determine the root cause(s) 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.
Data Science Will Help You Adjust to a Lean Manufacturing World
Data science is disrupting manufacturing in a big way right now. Even established manufacturing companies have adopted the practice to keep up. Lean manufacturing is the “norm” now, which causes companies to adopt continuous improvement programs at every stage to eliminate waste.
When done well, data science results in increased productivity and profit. To be successful, however, you’ll need to make sure that your data science team asks the right questions, and then determines and collects optimal metrics to help you meet your business goals.
Going further, you need to present the insights to end users who can use the predictions and recommendations to make more effective decisions. You may integrate this information into existing systems or build a stand-alone application dashboard to share the relevant data. At Very, we find it particularly effective to develop these applications with data scientists working as integrated members of a full-stack Industrial IoT development team.
With data science, manufacturing companies can create, price, and distribute products as efficiently as possible. This newfound efficiency will translate into improved competitiveness and an increased bottom line.