The benefits of applying data and analytics in manufacturing are substantial, particularly during times of disruption and uncertainty—such as volatile supply chains and changing market conditions. Manufacturers now 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 there be disruption large or small as data visibility is important to both scenarios. These changes and responses can extend from your supplier relationships to your customer experiences.
If you haven’t adopted a data strategy yet, you may be feeling pressure from all directions: competition that’s gaining market share, customers who expect you to anticipate their needs, suppliers’ changing lead times, an incoming workforce that has higher technology standards and an unpredictable market that demands better visibility over every aspect of your organization.
Because analytics are now an industry must-have, here are a few basics to help you determine their potential benefit to your organization.
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 and production efficiency.
According to Gartner, big data is defined as “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing.” This data “enables enhanced insights, decision making and process automation.” Big data is basically the sum of data as it pertains to a single entity. This volume of data doesn’t easily fit into a single spreadsheet, and it requires much more than human computational skills to make the best use of it.
Consider that, on a human level, you could only analyze a fraction of your big data. By the time you have made determinations from that sliver of information, an entirely new set of data has come in that might lead to different answers. By relying on human computational skills and spreadsheets alone, you end up always being a step or two behind the most relevant decisions and unable to make use of all the data available to you.
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. Many organizations skip over assessing a data warehouse and move straight to analytics, but the old adage of “garbage in, garbage out” applies here. If the data going into your reporting and analytics is inconsistent, duplicative and/or inaccurate, the insights gleaned from it will be too.
To make an informed decision for your processes, learn why top performing CFOs are turning to data warehouses.
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 the steps of your processes. There are many methods available, and the more sophisticated your data capture tools, the better and more relevant your data can be. For example, both manual and automatic mileage logs are methods of data capture. However, the automatic log is likely to be more accurate, efficient and immediate, although they both serve the same purpose.
Data analytics is the action of mining through data to find relevant patterns. Analytics can be used to detect issues as they arise, preempt issues that might occur and find areas in a process that can be improved. Advanced analytics tools amplify the possibilities to use the data you have.
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.
Having and analyzing data on the quality of your suppliers’ performance can help you make several determinations:
Manufacturers tend to have a close eye on their Order Cycle Time, while trying to lessen 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 as short and efficient as possible 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 and inventory 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, so 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.
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 the year. You can analyze key metrics like:
Collecting data from your equipment, which could include everything from production line machines to transport vehicles, and applying analytics helps you get proactive about maintenance and performance. Analytics can identify signals of potential upcoming problems and make suggestions for regular maintenance.
If you’re measuring and adjusting for metrics like perfect order performance, rate of returns, delivery times, Order Cycle Time and product quality, your efforts in these areas will improve the end-consumer experience.
You’ll be able to identify patterns that allow you to discover and respond to the “why” behind frequent customer issues 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.
Analytics along the supply chain can play a major role in helping you increase efficiencies in current processes. 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. Then you can quickly react accordingly.
With all the disruptions in the industry from shipping to damages to other issues, data gathering is key to identifying where the backlogs are occurring.
These are only some of the more common use cases for analytics in manufacturing. However, the possibilities in just these areas are enough to make a significant difference in business efficiencies. If you haven’t tapped into the potential of your big data and you’d like to learn more about getting started, reference our Data Analytics Playbook to get started.
Make sure your manufacturing organization is data-driven with these strategies.