If you’ve ever ordered a package from popular online retailers like Amazon, you’ve already experienced how data analytics can improve efficiency in logistics, package delivery, and overall customer satisfaction.
The same improvements are evident for manufacturing companies who are opting to use data analytics to improve their operations.
Let’s take a look at a few data analytics use cases in manufacturing.
Table of Contents
Extend The Life of Equipment
Maintaining manufacturing equipment is the most important part of the manufacturing process. Without working equipment, the likelihood of staying in business is low.
The good news is there are now data analytics options available for manufacturers that make it easier to streamline manufacturing processes. This includes manufacturing equipment management.
Using data analytics and intelligent software, frontline-manufacturing workers can proactively replace or repair equipment that shows an indication of being near its end date. Sensors built in to manufacturing equipment can alert the production team to let them know when it’s time to repair or replace equipment.
Predictive maintenance comes into play when these systems alert operators an action needs to be taken to preserve the integrity of the equipment and the overall manufacturing process. For example, a heavy machine may emit an alert to let the operator know it is nearly time for its preventive maintenance cycle. Time and money are saved when the machine operator complies with the maintenance request — before the machine breaks down.
Supply-Chain Management
Making sure manufacturing supplies, equipment, and products reach their intended destinations on time is another integral part of the manufacturing process. Data analytics are available to help make supply chain management and logistics processes more manageable.
Supply-chain management is one of the most critical components of manufacturing companies that can make or break a business. Making sure products and services are delivered at exactly the right place and exactly the right time is a tricky process. This process can be made easier by introduction of data analytics and data driven processes into the mix.
With data analytics, workers can autonomously make ad hoc decisions based on historical data. Manufacturing leads can also make assumptive predictions based on what has happened in the past and future decisions on how to best keep the supply chain moving forward.
Data analytics collected in real-time also make it easier for logistics providers and manufacturing teams to course correct when there is an issue with timely delivery or pick-up.
Customized Products
In the past, it would be more costly to produced customized products for a customer than it is now. Today, manufacturing companies can use a combination of hardware, software, and data analytics options to provide customized solutions for clients and partners. Technology allows for processes like printing, shipping, customization, engraving, and labeling, to be completed onsite and integrated with data-driven logistics processes that ensure timely delivery.
Automation and Ease of Use
Using data-driven solutions in manufacturing makes it easier for manufacturers of all sizes to easily automate processes and predict future business needs. This means management teams and workers can afford more time to other important aspects of manufacturing operations. Meanwhile analyzation, reporting, predictive maintenance, and software sensors work together to do the heavy lifting behind the scenes. This process of automation makes manufacturing services more efficient and can save time and money on trying to maintain paper-based or other outdated processes.
These data analytics use cases in manufacturing are a few of many examples. Today’s manufacturers are learning the value of using intelligent systems that can automate important processes. Using data analytics and data-driven processes is quickly becoming the norm as manufacturers realize more time and cost savings.
