The benefits of applying data and analytics in manufacturing are substantial, particularly during times of disruption and uncertainty like that caused by the COVID-19 pandemic. Even before the pandemic, many in the industry were getting on board and seeing quick results.
Now, more and more manufacturers see how essential data and analytics are to agility, strength and business continuity.
With the right data delivered to the right person in real time, you position yourself to make effective changes that can immediately improve your bottom line. You’re also empowered to respond quickly should drastic disruption occur. These changes and responses can extend from your supplier relationships to your customer experiences. Imagine the possibilities.
If you haven’t adopted an analytics strategy yet, you may be feeling pressure from all directions: competition that’s gaining the lead, customers who expect you to anticipate their needs, suppliers’ changing expectations, an incoming workforce that depends on analytics tools and an unpredictable market that demands better visibility over every aspect of your business.
Because analytics are quickly becoming an industry “must,” here are a few basics to help you determine their potential benefit to your business.
Defining Big Data in Manufacturing
Every manufacturing company, no matter how sophisticated their data collection, has a sizeable amount of information coming in on a regular basis. That data includes everything from equipment performance to product quality. Big data is the sum of this data as it pertains to a single entity. This data wouldn’t fit into a single spreadsheet, and it would take much more than human computational skills to make use of it.
Consider that, on a human level, you could only analyze a fraction of your big data. And by the time you have made determinations from that fraction, an entirely new set of data has come in that might have led to different answers. You are always a step or two behind the most relevant decisions and can’t make use of all the information.
To harness your big data for the most relevant and useful analytics, you need a proper data warehousing solution. A data warehouse centralizes this information so you can conduct thorough analysis and generate reports. There are ways to DIY your own data warehouse, but it’s often more expensive and less efficient than using a managed data warehouse solution. Plus, the information your data warehouse provides is only as good as the warehouse itself. To make an informed decision for your processes, learn the eight essential data warehouse best practices.
Learn why top-performing CFOs are turning to data warehouses.
Manufacturing Data Capture vs. Manufacturing Data Analytics
There are two areas of focus for making the most of your big data: data capture and data analytics.
Data capture is collecting information throughout your processes. There are many methods available, and the more sophisticated your data capture tools, the better and more relevant your data can be. As an example, both manual and automatic mileage logs are methods of data capture. The automatic log is likely to be more accurate, efficient and immediate, but both serve the same purpose.
Data analytics is the action of mining through the data to find relevant patterns and usable information. Analytics can be used to detect issues as they arise, preempt issues that might occur, find areas in a process that can be improved, and much more. Advanced analytics tools amplify the possibilities with the data you have.
Analytics Use Cases in Manufacturing
Since the possibilities are so vast with analytics, it can be difficult to narrow your focus. Here are just a few of the most common use cases for analytics in the manufacturing industry.
Qualify Suppliers by Quality
Having and analyzing data on the quality of your suppliers’ performance can help you make several determinations.
Optimize Time from Product Order to Receipt
Manufacturers tend to have a close eye on their “Order Cycle Time,” which Forbes describes as the time it takes for a product to move through the production process from customer order to customer receipt. It’s important to keep this time optimal and make improvements as needed. The Order Cycle Time is considered a metric of efficiency. Several other manufacturing efficiency metrics can fold into your analysis of the cycle, including the production process and inventory process.
Improve Production Processes
Data from along the production line can put definitive reasoning behind your yield rates. It can surface issues that are actively or passively reducing efficiency, and you can make the appropriate changes to improve production. On the other hand, it can also identify strengths along your production line and help you distribute them across the process.
Enhance Inventory Processes
You can use inventory data to transform everything from the way you arrange your storage to the level of stock you keep at certain times of year. You can analyze key metrics like:
Monitor And Maintain Equipment
Collecting data from your equipment, which could include everything from production line machines to transport vehicles, and applying analytics means you can get proactive about maintenance and performance. Analytics can identify signals of upcoming problems and make suggestions for regular maintenance.
Improve Customer Satisfaction
If you’re measuring and adjusting for metrics like perfect order performance, rate of returns, delivery times, Order Cycle Time and product quality – key metrics for analytics, according to Forbes – your efforts in these areas will improve the end-consumer experience, and you’ll be able to identify patterns and discover and respond to the “why” behind things like consistent returns or late deliveries. Also, when you have real-time information coming in, you can react to changes in your own supply chain and production process, reducing the impact on your customer satisfaction levels.
Trace And Streamline Your Supply Chain
Analytics along the supply chain can play a major role in helping you increase efficiencies in current processes. But it can also be one of the most useful applications of real-time manufacturing analytics. As you monitor the quality and output of suppliers along the chain, you’ll have more immediate knowledge of any changes that could impact your supply, such as delays or temporary closures.
Where to Start
These are only some of the more common use cases for analytics in manufacturing. Yet, the possibilities in just these areas are enough to make a significant difference in any business. Again, this is especially true today, as the COVID-19 pandemic continues to impact the industry. If you haven’t tapped the potential of your big data and you’d like to learn more about getting started, use the “quick start” worksheet to get going on your data journey.
Use this quick worksheet for five steps to start your data journey.